Assigning obstacles to lanes using neural networks for autonomous machine applications

ABSTRACT

In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

BACKGROUND

Autonomous and semi-autonomous driving systems, as well as advanced driver assistance systems (ADAS), may leverage sensors, such as cameras, to perform various tasks—such as lane keeping, lane changing, lane assignment, camera calibration, and localization. For example, for these systems to operate independently and efficiently, an understanding of the surrounding environment of the vehicle—in real-time or near real-time—may be generated. This understanding may include information as to locations of objects and/or obstacles in the environment with respect to various demarcations—such as lanes, road boundaries, intersections, and/or the like. The information of the surrounding environment may be used by a vehicle when making decisions, such as what path or trajectory to follow, whether to speed up or slow down, whether a lane change or other maneuver is currently safe, etc.

Conventional methods and systems have allowed for the detection of lanes as a separate task from the detection of objects, and then relied on post-processing to determine object to lane assignments. For example, these methods generally require the preliminary operations of detecting objects and estimating lanes prior to any assignment of the objects. In such systems, the post-processing of the identified lanes and objects requires significant use of computing resources and additional processing time which presents limitations to a real-time autonomous or semi-autonomous driving system. For example, processing sensor data to determine object locations, then processing sensor data to determine lane locations, and then synchronizing and aligning the two detection types requires increased processing, compute, and energy consumption. As a result, the constraints inherent in conventional systems may result in inaccurate lane estimation, especially in situations in which there are many objects present in the image data and/or where objects may be occluded by other objects or conditions of a roadway such as curves, dips, and/or hills. Such road characteristics can result in a poor lane estimation and thus cause inaccuracies when extending the estimated lanes onto detected objects or vehicles. In some conventional systems, lane assignment relies on analyzing 3D signals—such as from LiDAR, RADAR, and/or the like. However, these 3D signals, such as predicted object velocity, position, shape, etc., require additional sensing and analysis components in order to implement algorithms that can predict the intersection of detected objects and estimated lanes.

SUMMARY

Embodiments of the present disclosure relate to assigning obstacles to lanes using neural networks for autonomous machine applications.

Systems and methods are disclosed that provide approaches for training and deploying a machine learning model(s)—such as a deep neural network(s) (DNN(s))— as part of a single-shot deduction to determine an object classification label and a lane identifier for objects. For example, and in contrast to conventional systems, such as those described above, the current system may use the machine learning model(s) to determine the object classification label and the lane identifier for objects detected using sensor data generated using one or more sensors of an autonomous or semi-autonomous machine. In embodiments, the machine learning model(s) may be trained to compute outputs corresponding to object classifications and lane identifications—such as segmentation masks corresponding to lane regions of sensor data (e.g., image data)—and a post-processor may use this information to effectively and accurately determine object and lane classifications. The outputs may be post processed using various techniques—such as smoothing, clustering, thresholding, up-sampling, etc.—to determine object to lane assignments. These object to lane assignments may be used by the vehicle to safely and accurately navigate the roadway and other obstacles.

As a result of using the same machine learning model to assign objects to lanes—without having to use separate machine learning models for lane and object detection, as in conventional systems—processing time in both training and in runtime deployment is reduced. Further, deploying DNNs as part of a single-shot deduction results in more accurate estimation in a wider number of scenarios, as the error in combining separate object and lane computations (which may have been computed using different sensors at different times) of prior approaches is avoided.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for assigning obstacles to lanes using neural networks for autonomous machine applications are described in detail below with reference to the attached drawing figures, wherein:

FIG. 1 is an example data flow diagram illustrating an example process for training a neural network to generate output masks to aid assigning objects detected in sensor data to lanes, in accordance with some embodiments of the present disclosure;

FIGS. 2A-2D are visualizations of example annotations applied to sensor data and resulting ground truth masks used for training a neural network to assign objects to lanes, in accordance with some embodiments of the present disclosure;

FIG. 3 is a flow diagram illustrating an example method for training a neural network to assign objects to lanes, in accordance with some embodiments of the present disclosure;

FIG. 4 is a data flow diagram illustrating an example process for assigning obstacles to lanes using outputs of a deployed neural network, in accordance with some embodiments of the present disclosure;

FIGS. 5A-5B are visualizations of examples of generating object to lane assignments using outputs of a deployed neural network, in accordance with some embodiments of the present disclosure;

FIGS. 6-7 are flow diagrams illustrating example methods for assigning objects to lanes using a deployed neural network, in accordance with some embodiments of the present disclosure;

FIG. 8A is an illustration of an example autonomous vehicle, in accordance with some embodiments of the present disclosure;

FIG. 8B is an example of camera locations and fields of view for the example autonomous vehicle of FIG. 8A, in accordance with some embodiments of the present disclosure;

FIG. 8C is a block diagram of an example system architecture for the example autonomous vehicle of FIG. 8A, in accordance with some embodiments of the present disclosure;

FIG. 8D is a system diagram for communication between cloud-based server(s) and the example autonomous vehicle of FIG. 8A, in accordance with some embodiments of the present disclosure;

FIG. 9 is a block diagram of an example computing device suitable for use in implementing some embodiments of the present disclosure; and

FIG. 10 is a block diagram of an example data center suitable for use in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to obstacle to lane assignment using a neural network for autonomous machine applications. Although the present disclosure may be described with respect to an example autonomous vehicle 800 (alternatively referred to herein as “vehicle 800” or “ego-machine 800,” an example of which is described with respect to FIGS. 8A-8D), this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more advanced driver assistance systems (ADAS)), piloted and un-piloted robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. In addition, although the present disclosure may be described with respect to object to lane assignments, this is not intended to be limiting, and the systems and methods described herein may be used in augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, and/or any other technology spaces where object detection, classification, and region-based assignment may be used.

As described herein, in contrast to conventional systems, the present disclosure provides techniques to assign detected objects to lanes using a machine learning model—such as a deep neural network (DNN)—for autonomous machine applications. Systems and methods are disclosed that provide approaches for training and deploying a deep neural network(s) (DNN) as part of a single-shot deduction to determine an object classification label and a lane identifier for objects detected using sensor data generated using one or more sensors of an autonomous or semi-autonomous machine.

In deployment, the DNN may use input sensor data—such as image data—to compute one or more output masks that correspond to object classes (e.g., vehicles, cars, trucks, pedestrians, motorcycles, etc.), lane identifiers (e.g., ego-lane, right of ego-lane, left of ego-lane, other lane types, etc.), and/or combinations thereof. For example, in non-limiting embodiments, the DNN may output M×N number of masks, where M is a number of lane identifiers and N is a number of object classes. As such, for each combination of lane identifier and object class, the DNN may compute an output mask—such as a segmentation mask indicating the pixels of an image that correspond to the particular combination of object class and lane identifier. For example, where the DNN is trained to compute output masks for vehicle, pedestrian, and cyclist object classes, as well as ego-lane, left of ego-lane, right of ego-lane, and an “other” lane identifier, the DNN may compute twelve output masks—one for vehicle ego-lane, one for vehicle right lane, one for vehicle left lane, one for vehicle other lane, one for pedestrian ego-lane, one for pedestrian left lane, one for pedestrian left lane, one for pedestrian other lane, and so on. In some embodiments, the output masks may correspond to a segmentation mask that is computed at a resolution that is the same or different from a resolution of the input data representation (e.g., an image). In some embodiments, the output masks may be in two-dimensional (2D) image space, from the perspective of the image sensor. However, in other embodiments, the output masks may be in three-dimensional (3D) world space, such that the input data is from a top-down or bird's eye view representation of an environment around the ego-vehicle. As such, the pixel coordinates may correspond to (x, y) locations in 3D world space relative to an origin of the ego-vehicle.

From the output masks, an object class label and/or lane identifier label may be determined for one or more points represented by the sensor data. For example, an object class label and lane identifier label may be determined for pixels of an image captured by a camera or other imaging hardware. To determine which combination of object class and lane identifier corresponds to each detected object, a bounding shape may be computed either by the DNN and/or using another object detection algorithm (e.g., a computer vision algorithm, another DNN for object detection, etc.). As such, confidences from each of the masks for pixels inside the bounding shape may be evaluated, and for each pixel location, the confidences for that particular pixel in each mask output by the DNN may be compared to determine the highest confidence. For example, a point within the bounding shape of a first output mask for vehicle object class and left lane may have a confidence of 0.4 while the same point in a second output mask for pedestrian object class and left lane may have a confidence of 0.8. In such an example, the point or pixel may be determined to correspond to a combination of pedestrian and left of ego-lane. Once the combination of object class and lane identifier are determined for the points or pixels within a bounding shape, a final determination of the combination of the object class and lane identifier for the particular object may be determined. For example, in some embodiments, the combination of object class and lane identifier corresponding to the most pixels or points within the bounding shape may be used to determine the final assignment of object class and lane identifier. In other non-limiting examples, weighting may be performed, such that pixels or points closer to a center of the bounding shape may be weighted more heavily than pixels further from the center, and the weighted confidences may be used to determine the final assignment.

To train the DNN, one or more segmentation masks may be generated as ground truth to compare to the segmentation masks computed using the DNN. For example, as described above, the DNN may be trained to compute M×N output masks or channels. In some embodiments, each segmentation mask may be generated by determining one or more bounding shapes corresponding to an image and determining a set of pixels in the image that correspond to the one or more bounding shapes. For example, for a mask corresponding to a combination of vehicle object class and ego-lane lane identifier, each of the pixels inside a bounding shape labeled or annotated as being from the vehicle object class/ego-lane combination may be encoded with a confidence value of 1.0, while each pixel outside of the bounding shape that is not within another bounding shape of the same combination of object class and lane identifier may be encoded with a value of 0.0. To increase the accuracy of the DNN, in embodiments, only a portion of the pixels or points within the bounding shape may be encoded with the value of 1.0. For example, an ellipse may be generated within the bounding shape with the center of the bounding shape at the center of the ellipse, and only the points or pixels within the ellipse may be encoded with the confidence of 1.0 as to the associated combination of object class and lane identifier. In this way, some pixels or points within the bounding shape that may correspond to background and not the actual object may be removed from the encoding. Once the object class and lane identifier confidence values are assigned to the one or more segmentation masks, the DNN may be trained using this assignment as ground truth data. For example, the DNN may be trained using a loss function to compute a loss for each type of lane identifier, and the loss function may comprise computing a sum of each of the losses for multiple lane identifier types. In some embodiments, a loss function may comprise computing a loss for each of the pixels or points that corresponds to objects represented by sensor data. For example, a loss may be computed for each pixel that corresponds to one or more bounding shapes that are associated with one or more objects represented by the sensor data. As such, the DNN may be trained to directly compute object to lane assignments without requiring separate DNNs or detection algorithms for each of lanes and objects, and then post-processing to determine the assignments. In this way, training the DNN is less time consuming and requires less computations, and the single DNN in deployment reduces processing and energy requirements of the system, as well as reduces runtime.

Training a Machine Learning Model to Compute Obstacle to Lane Assignments

Now referring to FIG. 1 , FIG. 1 is an example data flow diagram illustrating an example process 100 for training a neural network to generate output masks to aid assigning objects detected in sensor data to lanes, in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. In some embodiments, the systems, methods, and processes described herein may be executed using similar components, features, and/or functionality to those of example autonomous vehicle 800 of FIGS. 8A-8D, example computing device 900 of FIG. 9 , and/or example data center 1000 of FIG. 10 .

At a high level, the process 100 may include one or more machine learning model(s) 104 receiving one or more inputs, such as sensor data 102, and generating one or more outputs, such as one or more output mask(s) 106. In some examples, when used for training, the sensor data 102 may be referred to as training data. Although the sensor data 102 is primarily discussed with respect to image data representative of images, this is not intended to be limiting, and the sensor data 102 may include other types of sensor data used for lane assignments, such as LiDAR data, SONAR data, RADAR data, and/or the like—e.g., as generated by one or more sensors of the vehicle 800 (FIGS. 8A-8D).

The process 100 may include generating and/or receiving sensor data 102 from one or more sensors. The sensor data 102 may be received, as a non-limiting example, from one or more sensors of a vehicle (e.g., vehicle 800 of FIGS. 8A-8D and described herein). The sensor data 102 may include, without limitation, sensor data 102 from any of the sensors of the vehicle including, for example and with reference to FIGS. 8A-8D, global navigation satellite systems (GNSS) sensor(s) 858 (e.g., Global Positioning System sensor(s)), RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDAR sensor(s) 864, inertial measurement unit (IMU) sensor(s) 866 (e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s) 896, stereo camera(s) 868, wide-view camera(s) 870 (e.g., fisheye cameras), infrared camera(s) 872, surround camera(s) 874 (e.g., 360 degree cameras), long-range and/or mid-range camera(s) 898, speed sensor(s) 844 (e.g., for measuring the speed of the vehicle 800), and/or other sensor types. As another example, the sensor data 102 may include virtual (e.g., simulated or augmented) sensor data generated from any number of sensors of a virtual vehicle or other virtual object in a virtual (e.g., testing) environment. In such an example, the virtual sensors may correspond to a virtual vehicle or other virtual object in a simulated environment (e.g., used for testing, training, and/or validating neural network performance), and the virtual sensor data may represent sensor data captured by the virtual sensors within the simulated or virtual environment. As such, by using the virtual sensor data, the machine learning model(s) 104 described herein may be tested, trained, and/or validated using simulated data from a simulated environment and/or augmented real-world data, which may allow for testing more extreme scenarios outside of a real-world environment where such tests may be less safe.

In some embodiments, the sensor data 102 may include image data representing an image(s), image data representing a video (e.g., snapshots of video), and/or sensor data representing representations of sensory fields of sensors (e.g., depth maps for LiDAR sensors, a value graph for ultrasonic sensors, etc.). Where the sensor data 102 includes image data, any type of image data format may be used, such as, for example and without limitation, compressed images such as in Joint Photographic Experts Group (JPEG) or Luminance/Chrominance (YUV) formats, compressed images as frames stemming from a compressed video format such as H.264/Advanced Video Coding (AVC) or H.265/High Efficiency Video Coding (HEVC), raw images such as originating from Red Clear Blue (RCCB), Red Clear (RCCC), or other type of imaging sensor, and/or other formats. In addition, in some examples, the sensor data 102 may be used within the process 100 without any pre-processing (e.g., in a raw or captured format), while in other examples, the sensor data 102 may undergo pre-processing (e.g., noise balancing, demosaicing, scaling, cropping, augmentation, white balancing, tone curve adjustment, etc., such as using a sensor data pre-processor (not shown)). As used herein, the sensor data 102 may reference unprocessed sensor data, pre-processed sensor data, or a combination thereof.

The sensor data 102 used for training may include original sensor data representations (e.g., as captured by one or more sensors), down-sampled representations (e.g., down-sampled images), up-sampled representations, cropped or region of interest (ROI) representations, otherwise augmented representations, and/or a combination thereof. The machine learning model(s) 104 may be trained using the sensor data 102 as well as corresponding ground truth data that may be generated by the ground truth generator 108. The ground truth generator 108 may be configured to use the sensor data 102 to generate ground truth data such as the GT mask(s) 116. In embodiments, to generate the ground truth data, the ground truth generator 108 may use annotations 110, a background eliminator 112, and a mask encoder 114. The ground truth data may include annotations, labels, masks, maps, and/or the like. For example, in some embodiments, the ground truth data may include ground truth (GT) mask(s) 116. A mask encoder 114 may use the annotations 110 to encode the GT mask(s) 116, and the annotations 110 may include object class label(s) 110A and lane identifier label(s) 110B.

With reference to the annotations 110, the annotations 110 may be generated within a drawing program (e.g., an annotation program), a computer aided design (CAD) program, a labeling program, another type of program suitable for generating the annotations 110 and/or the GT mask(s) 116, and/or may be hand drawn. In any example, the annotations 110 and/or the GT mask(s) 116 may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., labeler, or annotation expert, defines the location of the labels), and/or a combination thereof (e.g., human identifies center or origin point and dimensions of areas, machine generates polygons and/or labels for objects and/or lanes).

The object class label(s) 110A may include annotations, or other label types, corresponding to bounding shapes—e.g., polygons—delineating areas of interest of an environment represented by the sensor data 102. In some examples, objects such as vehicles, cars, pedestrians etc. may be delineated—e.g., within the sensor data representations of the sensor data 102—by one or more polygons corresponding to objects detected in the sensor data 102. The polygons may be generated as bounding boxes, ellipse, and/or any other shape that can be used to delineate the objects in the sensor data 102. The object class label(s) 110A may comprise annotations, or other label types, generated for each of the images (or other data representations) and/or for each one or more of the polygons in the images represented by the sensor data 102 used for training the machine learning model(s) 104. For example, the object class label(s) 110A may include generating a label associated with each polygon, with the labels providing information indicating a particular classification in which the detected objects corresponding to the bounding shapes belong (e.g., vehicles, cars, trucks, pedestrians, motorcycles, etc.).

The lane identifier label(s) 110B may be generated for each of the images (or other data representations) and/or for each one or more of the lanes in the images represented by the sensor data 102 used for training the machine learning model(s) 104. The number of lane identifier label(s) 110B may correspond to the number and/or types of lanes or lane features that the machine learning model(s) 104 is trained to predict, or to the number of lane regions and/or types of features in the respective image. Depending on the embodiment, the lane identifier label(s) 110B may correspond to classifications or tags corresponding to the lane identifiers, such as but not limited to ego-lane, right of ego-lane, left of ego-lane, other lane types, and/or the like.

The object class label(s) 110A and the lane identifier label(s) 110B may be configured into a combination of object class and lane identifier. For example, a combination of object class and lane identifier may be associated with a detected object represented by the sensor data 102. For instance, a detected object may be represented by the combination of a “vehicle” object class label and a “left of ego-lane” lane identifier label. In some embodiments, the ground truth generator 108 may be configured to generate a GT mask(s) 116 corresponding to each combination of object class and lane identifier that the machine learning model(s) 104 is being trained to compute an output mask 106 for. For example, the machine learning model(s) 104 may be trained to output M number of lane identifiers and N number of object classes. In such an example, the machine learning model(s) 104 may be configured for M×N combinations of object classes and lane identifiers. The ground truth generator 108 may use the annotations 110 including the object class label(s) 110A and the lane identifier label(s) 110B, to generate M×N GT mask(s) 116 corresponding to each combinations of object class and lane identifier. The labeling ontologies described herein are for example purposes only, and additional and/or alternative class labels or lane identifiers may be used without departing from the scope of the present disclosure.

As non-limiting examples, and with respect to FIGS. 2A-2D, FIGS. 2A-2D illustrate example annotations applied to sensor data and GT mask(s) 116 for use in the ground truth generator 108 for training a machine learning model to assign objects to lanes, in accordance with some embodiments of the present disclosure. For example, FIG. 2A illustrates an example image 200A that may be used to generate ground truth data in accordance with the training process 100 of FIG. 1 . The image 200A may be represented by the sensor data 102 and may depict any of a number of scenarios associated with objects, driving surfaces, and/or any other roadway component. For example, the image 200A may depict one or more objects such as vehicle objects 202A, 202B, 202C, and 202D. While FIG. 2A depicts the objects as vehicles, it is not intended to be limiting, as any detected object, such as structures, animals, pedestrians, etc. are contemplated herein. The image 200A may comprise one or more lanes corresponding to the driving surfaces represented in the image 200A. For example, lanes 204, 206, 208, and 210 are depicted in image 200A and may correspond to a location relative to reference point, such as an ego-vehicle associated with the sensor data 102. From the perspective of an ego-vehicle, the lanes 204, 206, 208, and 210 may correspond to an ego-lane, a left of ego-lane, a right of ego-lane, and other lane. For instance, if an ego-vehicle is positioned in lane 204, lane 204 may be associated with the ego-lane, lane 206 may correspond to a left of ego-lane, lane 208 may correspond to a right of ego-lane, and lane 210 may correspond to an “other” lane label.

Referring now to FIG. 2B, FIG. 2B illustrates an example of an annotated image 200B with annotations applied to sensor data to train a machine learning model to assign objects to lanes, in accordance with some embodiments of the present invention. As depicted here, the vehicle objects 202A, 202B, 202C, and 202D of FIG. 2A, may be annotated with polygons or bounding shapes. For example, FIG. 2B depicts bounding shapes 212A, 212B, 212C, and 212D applied to the sensor data. In some examples, the bounding shape may be implemented as a bounding box, as in bounding shapes 212A, 212B and 212D, and/or may be implemented as any other shape or polygon, such as a bounding ellipse, as in bounding shape 212C.

Referring now to FIG. 2C, FIG. 2C illustrates an example of a segmentation mask 200C applied to sensor data to train a machine learning model to assign objects to lanes, in accordance with some embodiments of the present invention. As depicted here, a segmentation mask, such as the GT mask(s) 116 of FIG. 1 , may be generated using the annotations 110. The segmentation mask may represent the lane assignment output of the ground truth generator 108. The segmentation mask 200C may depict the lane assignment of pixels of the input image associated with the detected objects and the corresponding bounding shapes. The segmentation mask 200C may depict the assignment of pixels to a single lane classification, or may depict the assignment of pixels to a plurality of lane classifications. For example, pixels in pixel area 222A may be assigned to the left of an ego-lane, the pixels in pixel area 222B may be assigned to the ego-lane, and the pixels of pixel area 222C may be assigned to an other lane. In such an example, a first GT mask 116 may be generated for a vehicle class/left of ego-lane combination such that the pixels corresponding to object 222A may be included in the first GT mask 116. A second GT mask 116 may be generated for a vehicle to ego-lane combination such that the pixels corresponding to object 22B may be included in the second GT mask 116. This process may be repeated for each object class/lane combination to generate the GT masks 116 for each instance of the sensor data 102. The lane assignments may be of similar visual representation for a same classification. The different lane assignments may be represented in FIG. 2C by colors, solid lines, dashed lines, etc., to represent different classifications. This is not intended to be limiting, and any convention for indicating lane assignments may be used to illustrate differences in lane classifications represented in the images.

Referring now to FIG. 2D, FIG. 2D illustrates an example of a combined segmentation mask 200D with an eliminated background applied to sensor data to train a machine learning model to assign objects to lanes, in accordance with some embodiments of the present invention. As depicted here, the segmentation mask 200D may depict the lane assignment of pixels of the input image associated with the detected objects and the corresponding bounding shapes. The segmentation mask 200D may have the background portions of the input image eliminated, or otherwise masked, so that only the pixels corresponding to detected objects remain. For example, the pixel areas 232A, 232B, and 232C may retain the visual representation of a lane assignment to the corresponding pixels, while the pixels that are not included in pixel areas 232A, 232B, and 232C may be eliminated as the background 234, such as by the background eliminator 112 of FIG. 1 . As mentioned, segmentation mask 200D includes a combined segmentation mask, which may be used in embodiments as the GT mask 116. However, in other embodiments, individual GT masks 116 corresponding to each object class/lane combination may be generated, and the segmentation mask 200D may provide a combined visualization of the individual GT masks 116.

Referring again to FIG. 1 , the mask encoder 114 may be configured to encode the ground truth information corresponding to the detected objects and lanes using the annotation(s) 110. The annotation(s) 110 may be encoded by the mask encoder 114 to generate the ground truth data, such as the ground truth (GT) mask(s) 116. In addition, because the spatial resolution of the sensor data 102 being applied to the machine learning model(s) 104 may be different from—e.g., greater than, less than, etc.—the output spatial resolution corresponding to predictions of the machine learning model(s) 104, the ground truth information may be encoded into various formats.

For example, the annotation(s) 110 may be encoded in the form of GT mask(s) 116—such that each lane classification (e.g., ego-lane, left of ego-lane, right of ego-lane, etc.) and/or object classification (e.g., car, truck, motorcycle, pedestrian, etc.) is encoded in a separate GT mask. The object class label(s) 110A and the corresponding lane identifier label(s) 110B may be used by the encoder 112 to generate GT mask(s) 116, each corresponding to a particular object class and lane identifier determined from the annotation(s) 110. In some examples, each pixel in a GT mask(s) 116 may be encoded with a value corresponding to a lane identifier (e.g., within the bounding shapes associated with detected objects) of an object class label(s) 110A. In such examples, the pixels inside the object class label(s) 110A (e.g., bounding shape) may also be encoded with values indicating a lane identifier, while the pixels outside the object class label(s) 110A may be encoded with a zero value for generating the GT mask(s) 116. The ground truth may thus provide a continuous valued representation of each pixel in the image, allowing up-sampling of the resulting detections during deployment to be performed efficiently and accurately. Further, encoding each object classification for each lane identifier into its own GT mask 116 may allow for recall and intensification of individual instances of roadway scenarios for each class with accuracy and precision.

Once the GT mask(s) 116 is generated for each instance of the sensor data 102 (e.g., for each image where the sensor data 102 includes image data), the machine learning model(s) 104 may be trained using the GT mask(s) 116. For example, the machine learning model(s) 104 may generate output mask(s) 106, and the output mask(s) 106 may be compared—using the loss function(s) 118—to the GT mask(s) 116 corresponding to the respective instance of the sensor data 102. As such, feedback from the loss function(s) 118 may be used to update parameters (e.g., weights and biases) of the machine learning model(s) 104 in view of the GT mask(s) 116 until the machine learning model(s) 104 converges to an acceptable or desirable accuracy. Using the process 100, the machine learning model(s) 104 may be trained to accurately predict the output mask(s) 106 (and/or associated lane assignments) from the sensor data 102 using the loss function(s) 118 and the GT masks(s) 116. In some examples, different loss functions 118 may be used to train the machine learning model(s) 104 to predict different output mask(s) 106 for each object class type (e.g., car, truck, cyclist, motorcycle, pedestrian, etc.). For example, a first loss function(s) 118 may be used for comparing the output masks(s) 106 and the GT masks(s) 116 for a first object class type and a second loss function(s) 118 may be used for comparing the output mask(s) 106 and the GT mask(s) 116 for a second object class type. As such, in non-limiting embodiments, one or more of the output channels can be trained using a different loss function(s) 118 than another of the output channels.

The machine learning model(s) 104 may use the sensor data 102 to compute the output mask(s) 106, which may ultimately be applied to a decoder or one or more other post-processing components (described in more detail herein at least with respect to FIG. 4 ) in deployment to generate output masks, classifications, and/or other information. Although examples are described herein with respect to using deep neural networks (DNNs), and specifically convolutional neural networks (CNNs), as the machine learning model(s) 104 (e.g., with respect to FIGS. 1 and 4 ), this is not intended to be limiting. For example, and without limitation, the machine learning model(s) 104 may include any type of machine learning model, such as a machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, long/short term memory/LSTM, Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), areas of interest detection algorithms, computer vision algorithms, and/or other types of machine learning models.

As an example, such as where the machine learning model(s) 104 include a CNN, the machine learning model(s) 104 may include any number of layers. One or more of the layers may include an input layer. The input layer may hold values associated with the sensor data 102 (e.g., before or after post-processing). For example, when the sensor data 102 is an image, the input layer may hold values representative of the raw pixel values of the image(s) as a volume (e.g., a width, a height, and color channels (e.g., RGB), such as 32×32×3).

One or more layers may include convolutional layers. The convolutional layers may compute the output of neurons that are connected to local regions in an input layer, each neuron computing a dot product between their weights and a small region they are connected to in the input volume. A result of the convolutional layers may be another volume, with one of the dimensions based on the number of filters applied (e.g., the width, the height, and the number of filters, such as 32×32×12, if 12 were the number of filters).

One or more of the layers may include a rectified linear unit (ReLU) layer. The ReLU layer(s) may apply an elementwise activation function, such as the max (0, x), thresholding at zero, for example. The resulting volume of a ReLU layer may be the same as the volume of the input of the ReLU layer.

One or more of the layers may include a pooling layer. The pooling layer may perform a down sampling operation along the spatial dimensions (e.g., the height and the width), which may result in a smaller volume than the input of the pooling layer (e.g., 16×16×12 from the 32×32×12 input volume).

One or more of the layers may include one or more fully connected layer(s). Each neuron in the fully connected layer(s) may be connected to each of the neurons in the previous volume. The fully connected layer may compute class scores, and the resulting volume may be 1×1×number of classes. In some examples, the CNN may include a fully connected layer(s) such that the output of one or more of the layers of the CNN may be provided as input to a fully connected layer(s) of the CNN. In some examples, one or more convolutional streams may be implemented by the machine learning model(s) 104, and some or all of the convolutional streams may include a respective fully connected layer(s).

In some non-limiting embodiments, the machine learning model(s) 104 may include a series of convolutional and max pooling layers to facilitate image feature extraction, followed by multi-scale dilated convolutional and up-sampling layers to facilitate global context feature extraction.

Although input layers, convolutional layers, pooling layers, ReLU layers, and fully connected layers are discussed herein with respect to the machine learning model(s) 104, this is not intended to be limiting. For example, additional or alternative layers may be used in the machine learning model(s) 104, such as normalization layers, SoftMax layers, and/or other layer types.

In embodiments where the machine learning model(s) 104 includes a CNN, different orders and/or numbers of the layers of the CNN may be used depending on the embodiment. In other words, the order and number of layers of the machine learning model(s) 104 is not limited to any one architecture.

In addition, some of the layers may include parameters (e.g., weights and/or biases), such as the convolutional layers and the fully connected layers, while others may not, such as the ReLU layers and pooling layers. In some examples, the parameters may be learned by the machine learning model(s) 104 during training. Further, some of the layers may include additional hyper-parameters (e.g., learning rate, stride, epochs, etc.), such as the convolutional layers, the fully connected layers, and the pooling layers, while other layers may not, such as the ReLU layers. The parameters and hyper-parameters are not to be limited and may differ depending on the embodiment.

Now referring to FIG. 3 , each block of method 300, described herein, comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The method 300 may also be embodied as computer-usable instructions stored on computer storage media. The method 300 may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, method 300 is described, by way of example, with respect to the process 100 of FIG. 1 . However, the method 300 may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein.

FIG. 3 is a flow diagram showing a method 300 for training a neural network to assign objects to lanes, in accordance with some embodiments of the present disclosure. The method 300, at block B302, includes determining one or more bounding shapes corresponding to an image. For example, a bounding box or bounding ellipse may be determined in relation to the sensor data 102, where the sensor data 102 includes image data representing an image depicting one or more objects and/or lanes.

The method 300, at block B304, includes generating one or more segmentation masks. For example, the ground truth generator 108 may be used to generate one or more GT masks 116 for training the machine learning model(s) 104. The generating of the one or more segmentation masks may comprise performing the processes of blocks B306, B308, and B310 for each segmentation mask of the one or more segmentation masks.

The method 300, at block B306, includes determining at least one bounding shape of the one or more bounding shapes that corresponds to a respective object class and a lane identifier associated with the segmentation mask. For example, a bounding shape associated with the sensor data 102 may be determined to correspond to generated annotations 110 including the object class label(s) 110A and the lane identifier label(s) 110B.

The method 300, at block B308, includes determining at least a subset of pixels of the image corresponds to the at least one bounding shape. For example, the mask encoder 114 may associate the pixels within the bounding shape corresponding to the generated annotations 110 including the object class label(s) 110A.

The method 300, at block B310, includes assigning an object class and a lane identifier corresponding to the respective combination of object class and lane identifier to at least the subset of pixels. For example, the pixels within a bounding shape may be assigned the object class corresponding to the object class label(s) 110A and may be assigned the lane identifier label(s) 110B.

The method 300, at block B312, includes training a neural network using the one or more segmentation masks as ground truth data. For example, using the loss function(s) 118, the machine learning model(s) 104 may be trained using the sensor data 102 and the generated GT mask(s) 116 to generate one or more output mask(s) 106.

Determining Obstacle to Lane Assignments in Deployment

Referring now to FIG. 4 , FIG. 4 is a data flow diagram illustrating an example process 400 for assigning obstacles to lanes using outputs of a neural network in real-time or near real-time, in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. In some embodiments, the systems, methods, and processes described herein may be executed using similar components, features, and/or functionality to those of example autonomous vehicle 800 of FIGS. 8A-8D, example computing device 900 of FIG. 9 , and/or example data center 1000 of FIG. 10 .

The sensor data 102 may be similar to that described herein at least with respect to FIG. 1 . During deployment, the sensor data 102 may be applied to the machine learning model(s) 104—e.g., that has been trained according to the process 100—to compute output mask(s) 106 (and corresponding lane assignments) using the sensor data 102. As such, the machine learning model(s) 104 may output—or regress on—one or more of the output mask(s) 106, as described in more detail at least with respect to the output mask(s) 106 in FIG. 1 . For each, the machine learning model(s) 104 may compute a separate output mask 106 for each object class/lane identifier combination, and these output masks 106 may be used to determine object to lane assignments for each detected object. In other embodiments, however, the output mask(s) 106 may correspond to each object class and each lane identifier, or to other combinations of object class(es) and lane identifier(s).

The output mask(s) 106 may be applied to a post-processor 402 to generate object to lane assignments 408. In some examples, the post-processor 402 may use one or more post-processing algorithms (e.g., temporal smoothing, up-sampling, thresholding, clustering, mapping, etc.) to compute an object to lane assignments 408. For example, the post-processor 402 may use the output mask(s) 106 to generate a representation of the road surface, which may be used, in turn, to navigate the roadway—e.g., by control component(s) 410 of the vehicle. The post-processor may use the bounding shapes 406 of objects detected using an object detector 404 to generate the object to lane assignments 408. The object to lane assignments 408 may include classifications of detected objects, assignments of the detected objects to one or more lanes, and/or other information corresponding to the sensor data 102.

The post-processor may perform smoothing, up-sampling, and/or mapping to generate the object to lane assignments 408. In some embodiments, the output mask(s) 106 may undergo smoothing on a pixel-by-pixel basis to smooth out sharp peaks and fluctuations in the pixel values. In some examples, the smoothing may include a Gaussian smoothing of each of the output mask(s) 106 output by the machine learning model(s) 104. The sharp peaks and fluctuations in the output mask(s) 106 may be smoothed by applying a smoothing algorithm on each output mask(s) 106 to generate corresponding smoothed output mask(s).

In some embodiments, one or more of the output mask(s) 106 and/or smoothed output mask(s) may undergo up-sampling. For example, the output mask(s) may be generated at a first spatial resolution (e.g., a down-sampled spatial resolution) that is different from a second spatial resolution corresponding to the input spatial resolution of the sensor data 102. As such, the up-sampling may decode the data from the first spatial resolution of the output mask(s) 106 and convert or translate the data to the second spatial resolution of the sensor data 102. In some examples, bi-linear up-sampling may be applied to each of the output mask(s) 106 and/or each of the smoothed output mask(s) to recover the original resolution of the sensor data 102 and to generate corresponding up-sampled output mask(s). As a result, the processing speeds of the machine learning model(s) 104 may be increased—e.g., due to processing reduced spatial resolution data—while simultaneously preserving compute resources for other tasks of the system. In addition, by increasing processing speeds, the run-time of the system may be decreased thereby enabling real-time or near real-time deployment of the process 400 within the system (e.g., the vehicle 800).

The output mask(s) 106, the smoothed output mask(s), and/or the up-sampled output mask(s) may then be mapped to the bounding shapes 406 representing the detected objects represented in the sensor data 102, based on the negative and positive values associated with each pixel in the corresponding output mask(s). The mapping may include applying the output mask(s) 106 to a thresholding and/or clustering algorithm. In some embodiments, a watershed clustering algorithm may be used as the mapping to generate object to lane assignments 408 corresponding to each combination of object class and lane identifier area for each output mask(s) 106. In such embodiments, for detected object and the associated bounding shape 406, the pixels may be filtered out based on whether the pixels are located in association with the bounding shapes 406. For example, pixels that are outside of a bounding shape may be removed, or ignored. The remaining pixels may be used to determine that each pixel that is associated with a lane identifier. For example, for each pixel that remains within an associated bounding shape 406, the classifications for object type and/or lane identifier may be used to determine which object type/lane identifier combination is most heavily represented (e.g., has the most pixels with the highest confidence) within the pixels of the bounding shape 406. The object class/lane identifier combination that has the most associated pixels (e.g., with the highest confidence for the object class/lane identifier combination) may be used to assign the object class and lane identifier to the object. In some embodiments, a different technique may be used by the post-processor, such as to weight confidence for object class/lane identifier combinations of pixels closer to a center of the bounding shape higher than those further from the center. In other embodiments, an associated object class/lane identifier combination of a center pixel, or a group of pixels in the center of the bounding shape 406, may be relied upon to determine the object to lane assignments 408. As such, the output of the post-processor 402 may be object to lane assignments 408 (in addition to object class information) for each detected object represented in the sensor data 102.

With reference to FIGS. 5A-5B, FIGS. 5A-5B illustrate examples of generating object to lane assignments, in accordance with some embodiments of the present disclosure. For example, FIGS. 5A-5B illustrate example images that may be used to generate lane assignments in accordance with the process 400 for assigning obstacles to lanes of FIG. 4 . FIGS. 5A-5B depict input images 510A and 510B which may correspond to the sensor data 102 and may represent objects present within the immediate roadway environment of an ego-machine 800—such as objects 502A, 502B, 502C, and 502D of input image 510A and objects 512A and 512B of input image 510B.

FIG. 5A illustrates a visualization 520A of the image 510A with corresponding bounding shapes 504A, 504B, 504C, and 504D as determined using the machine learning model(s) 104 and/or the object detector 404. The visualization 530A corresponds to a combined output segmentation mask (corresponding to output masks 106) indicating pixel confidences for different object class/lane identifier combinations. In this example, the visualization 530A includes four classifications of object class/lane identifier for the pixels of the corresponding input image: pixel group 506A corresponds to an assignment of vehicles to the left of ego-lane, pixel group 506B corresponds to an assignment of vehicles to the ego-lane, pixel group 506C corresponds to an assignment of vehicles to the right of ego-lane, and pixel group 506D corresponds to an assignment of vehicles to other lanes. As such, during post-processing, the pixels for each output mask 106 (represented as a combined visual in the visualization 530A) within a given bounding shape may be analyzed to determine the object class/lane identifier combination that is most heavily represented within the bounding shape. For example, for bounding shape 504C, the most pixels within the bounding shape 504C may correspond to the pixel group 506C, and thus the object corresponding to the bounding shape 504C may be assigned a vehicle class label positioned in the right of ego-lane.

FIG. 5B illustrates a visualization 520B of the image 510B with corresponding bounding shapes 514A and 514B as determined using the machine learning model(s) 104 and/or the object detector 404. The visualization 530B corresponds to a combined output segmentation mask (corresponding to output masks 106) indicating pixel confidences for different object class/lane identifier combinations. In this example, the visualization 530B includes two classifications of object class/lane identifier for the pixels of the corresponding input image: pixel group 516A corresponds to an assignment of vehicles to the ego-lane and pixel group 516B corresponds to an assignment of vehicles to the right of ego-lane. As such, during post-processing, the pixels for each output mask 106 (represented as a combined visual in the visualization 530B) within a given bounding shape may be analyzed to determine the object class/lane identifier combination that is most heavily represented within the bounding shape. For example, for bounding shape 514A, the most pixels within the bounding shape 514A may correspond to the pixel group 516A, and thus the object corresponding to the bounding shape 514A may be assigned a vehicle class label positioned in the ego-lane.

Referring again to FIG. 4 , the most prevalent lane and object class or identifier assignment within any given bounding shape 406 may be used to determine an assignment of class label and lane location to each detected object. Once assigned, the object to lane assignments 408 may be used to perform one or more operations by a control component(s) 410 of the vehicle 800. As non-limiting examples, a perception layer of an autonomous driving software stack may update information about the environment based on the lane assignment information, a world model manager may update the world model to reflect the location, distance, attributes, and/or other information about a roadway(s), lanes, and other vehicle(s), and/or a control layer may use the information for determining controls when approaching, navigating through, and/or exiting the roadway(s) (e.g., based on attributes such as wait conditions, contention areas, vehicles with priority, size of the roadway, distance to the objects, etc.).

Now referring to FIGS. 6-7 , each block of methods 600 and 700, described herein, comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods 600 and 700 may also be embodied as computer-usable instructions stored on computer storage media. The methods 600 and 700 may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, methods 600 and 700 are described, by way of example, with respect to the process of FIG. 4 . However, these methods 600 and 700 may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein.

Referring to FIG. 6 , FIG. 6 is a flow diagram showing a method 600 for assigning object to lanes, in accordance with some embodiments of the present disclosure. The method 600, at block B602, includes computing, using a neural network and based at least in part on sensor data generated using one or more sensors, one or more output masks, each output mask of the one or more output masks corresponding to an object class and a lane identifier. For example, the sensor data 102 may be applied to the machine learning model(s) 104, and the machine learning model(s) 104 may be used to compute one or more outputs masks 106.

The method 600, at block B604, includes determining at least one object class label and at least one lane identifier label for one or more points represented by the sensor data based at least in part on the one or more output masks. For example, the post-processor 402 may compare, for each pixel in each output mask 106, the confidences for each object class/lane identifier combination corresponding to that pixel, and may determine the combination with the highest confidence. This process may be repeated for each pixel, or may be performed only for each pixel location within a computed bounding shape.

The method 600, at block B606, includes determining a bounding shape corresponding to at least one object based at least in part on the sensor data. For example, the object detector 404 and/or the machine learning model 104 may generate one or more bounding shapes 406 that correspond to objects detected from the sensor data 102.

The method 600, at block B608, includes associating the one or more points with the bounding shape. For example, pixels that are located within the bounding shapes may be associated with that particular bounding shape.

The method 600, at block B610, includes assigning an object class and a lane identifier to the object based at least in part on the at least one object class label and the at least one lane identifier label associated with the one or more points. For example, the largest set of pixels for a given object class/lane identifier combination within each bounding shape 406 may be used to assign the object class and lane identifier to the corresponding object.

Now referring to FIG. 7 , FIG. 7 is a flow diagram showing a method 700 for assigning object to lanes, in accordance with some embodiments of the present disclosure. The method 700, at block B702, includes computing, using a neural network and based at least in part on image data representative of an image generated using one or more sensors, one or more output masks, each output mask of the one or more output masks corresponding to respective combination of object class and lane identifier. For example, sensor data 102 may be applied to the machine learning model(s) 104 to generate one or more output mask(s) 106 for each combination of object class and lane identifier. The sensor data 102 may represent a roadway environment in a field of view of an image sensor of the vehicle 800.

The method 700, at block B704, includes determining a first combination of object class and lane identifier corresponding to a first set of pixels of the image and a second combination of object class and lane identifier corresponding to a second set of pixels of the image. For example, the output masks 106 may include a first output mask associated with a first combination of object class and lane identifier and a second output mask 106 associated with a second combination of object class and lane identifier. The confidences for each pixel may be determined by comparing an associated confidence for the pixel from the first output mask 106 to an associated confidence for the pixel from the second output mask 106, and determining the final combination based on which confidence is higher. This may be repeated for each pixel, or may be repeated for each pixel that is within a bounding shape.

The method 700, at block B706, includes determining a bounding shape corresponding to at least one object based at least in part on the sensor data. For example, using the object detector 404 and/or the machine learning model(s) 104, the bounding shapes 406 may be generated to correspond to objects detected using the sensor data 102.

The method 700, at block B708, includes associating the first set of pixels with the bounding shape based at least in part on the first set of pixels including more pixels than the second set of pixels within the bounding shape. For example, the number of pixels within a bounding shape corresponding to a first combination of object class and lane identifier may be compared to the number of pixels, within the same bounding shape, corresponding to a second combination of object class and lane identifier to determine the primary or prevailing object class and lane identifier combination associated with a particular detected object associated with the bounding shape.

The method 700, at block B710, includes assigning the first combination of object class and lane identifier to the object based at least in part on the association. For example, the object to lane assignments 408 may be generated based on determining that a greater number of pixels in a bounding shape are associated with a particular object class and lane identifier combination.

The method 700, at block B712, includes performing, using the one or more processing units, one or more operation for an autonomous machine based at least in part on the assignment of the first combination of object class and lane identifier to the object. For example, based on the object to lane assignments 408, the control component(s) 410 may perform or more operations associated with an autonomous machine.

Example Autonomous Vehicle

FIG. 8A is an illustration of an example autonomous vehicle 800, in accordance with some embodiments of the present disclosure. The autonomous vehicle 800 (alternatively referred to herein as the “vehicle 800”) may include, without limitation, a passenger vehicle, such as a car, a truck, a bus, a first responder vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police vehicle, an ambulance, a boat, a construction vehicle, an underwater craft, a drone, a vehicle coupled to a trailer, and/or another type of vehicle (e.g., that is unmanned and/or that accommodates one or more passengers). Autonomous vehicles are generally described in terms of automation levels, defined by the National Highway Traffic Safety Administration (NHTSA), a division of the US Department of Transportation, and the Society of Automotive Engineers (SAE) “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep. 30, 2016, and previous and future versions of this standard). The vehicle 800 may be capable of functionality in accordance with one or more of Level 3-Level 5 of the autonomous driving levels. The vehicle 800 may be capable of functionality in accordance with one or more of Level 1-Level 5 of the autonomous driving levels. For example, the vehicle 800 may be capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and/or full automation (Level 5), depending on the embodiment. The term “autonomous,” as used herein, may include any and/or all types of autonomy for the vehicle 800 or other machine, such as being fully autonomous, being highly autonomous, being conditionally autonomous, being partially autonomous, providing assistive autonomy, being semi-autonomous, being primarily autonomous, or other designation.

The vehicle 800 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle 800 may include a propulsion system 850, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type. The propulsion system 850 may be connected to a drive train of the vehicle 800, which may include a transmission, to enable the propulsion of the vehicle 800. The propulsion system 850 may be controlled in response to receiving signals from the throttle/accelerator 852.

A steering system 854, which may include a steering wheel, may be used to steer the vehicle 800 (e.g., along a desired path or route) when the propulsion system 850 is operating (e.g., when the vehicle is in motion). The steering system 854 may receive signals from a steering actuator 856. The steering wheel may be optional for full automation (Level 5) functionality.

The brake sensor system 846 may be used to operate the vehicle brakes in response to receiving signals from the brake actuators 848 and/or brake sensors.

Controller(s) 836, which may include one or more system on chips (SoCs) 804 (FIG. 8C) and/or GPU(s), may provide signals (e.g., representative of commands) to one or more components and/or systems of the vehicle 800. For example, the controller(s) may send signals to operate the vehicle brakes via one or more brake actuators 848, to operate the steering system 854 via one or more steering actuators 856, to operate the propulsion system 850 via one or more throttle/accelerators 852. The controller(s) 836 may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals, and output operation commands (e.g., signals representing commands) to enable autonomous driving and/or to assist a human driver in driving the vehicle 800. The controller(s) 836 may include a first controller 836 for autonomous driving functions, a second controller 836 for functional safety functions, a third controller 836 for artificial intelligence functionality (e.g., computer vision), a fourth controller 836 for infotainment functionality, a fifth controller 836 for redundancy in emergency conditions, and/or other controllers. In some examples, a single controller 836 may handle two or more of the above functionalities, two or more controllers 836 may handle a single functionality, and/or any combination thereof.

The controller(s) 836 may provide the signals for controlling one or more components and/or systems of the vehicle 800 in response to sensor data received from one or more sensors (e.g., sensor inputs). The sensor data may be received from, for example and without limitation, global navigation satellite systems sensor(s) 858 (e.g., Global Positioning System sensor(s)), RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDAR sensor(s) 864, inertial measurement unit (IMU) sensor(s) 866 (e.g., accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s), etc.), microphone(s) 896, stereo camera(s) 868, wide-view camera(s) 870 (e.g., fisheye cameras), infrared camera(s) 872, surround camera(s) 874 (e.g., 360 degree cameras), long-range and/or mid-range camera(s) 898, speed sensor(s) 844 (e.g., for measuring the speed of the vehicle 800), vibration sensor(s) 842, steering sensor(s) 840, brake sensor(s) (e.g., as part of the brake sensor system 846), and/or other sensor types.

One or more of the controller(s) 836 may receive inputs (e.g., represented by input data) from an instrument cluster 832 of the vehicle 800 and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 834, an audible annunciator, a loudspeaker, and/or via other components of the vehicle 800. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the HD map 822 of FIG. 8C), location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects and status of objects as perceived by the controller(s) 836, etc. For example, the HMI display 834 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.).

The vehicle 800 further includes a network interface 824 which may use one or more wireless antenna(s) 826 and/or modem(s) to communicate over one or more networks. For example, the network interface 824 may be capable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The wireless antenna(s) 826 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.), using local area network(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox, etc.

FIG. 8B is an example of camera locations and fields of view for the example autonomous vehicle 800 of FIG. 8A, in accordance with some embodiments of the present disclosure. The cameras and respective fields of view are one example embodiment and are not intended to be limiting. For example, additional and/or alternative cameras may be included and/or the cameras may be located at different locations on the vehicle 800.

The camera types for the cameras may include, but are not limited to, digital cameras that may be adapted for use with the components and/or systems of the vehicle 800. The camera(s) may operate at automotive safety integrity level (ASIL) B and/or at another ASIL. The camera types may be capable of any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. The cameras may be capable of using rolling shutters, global shutters, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red clear clear clear (RCCC) color filter array, a red clear clear blue (RCCB) color filter array, a red blue green clear (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensors (RGGB) color filter array, a monochrome sensor color filter array, and/or another type of color filter array. In some embodiments, clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or an RBGC color filter array, may be used in an effort to increase light sensitivity.

In some examples, one or more of the camera(s) may be used to perform advanced driver assistance systems (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a Multi-Function Mono Camera may be installed to provide functions including lane departure warning, traffic sign assist and intelligent headlamp control. One or more of the camera(s) (e.g., all of the cameras) may record and provide image data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, such as a custom designed (3-D printed) assembly, in order to cut out stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirrors) which may interfere with the camera's image data capture abilities. With reference to wing-mirror mounting assemblies, the wing-mirror assemblies may be custom 3-D printed so that the camera mounting plate matches the shape of the wing-mirror. In some examples, the camera(s) may be integrated into the wing-mirror. For side-view cameras, the camera(s) may also be integrated within the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment in front of the vehicle 800 (e.g., front-facing cameras) may be used for surround view, to help identify forward facing paths and obstacles, as well aid in, with the help of one or more controllers 836 and/or control SoCs, providing information critical to generating an occupancy grid and/or determining the preferred vehicle paths. Front-facing cameras may be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. Front-facing cameras may also be used for ADAS functions and systems including Lane Departure Warnings (LDW), Autonomous Cruise Control (ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration, including, for example, a monocular camera platform that includes a CMOS (complementary metal oxide semiconductor) color imager. Another example may be a wide-view camera(s) 870 that may be used to perceive objects coming into view from the periphery (e.g., pedestrians, crossing traffic or bicycles). Although only one wide-view camera is illustrated in FIG. 8B, there may any number of wide-view cameras 870 on the vehicle 800. In addition, long-range camera(s) 898 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not yet been trained. The long-range camera(s) 898 may also be used for object detection and classification, as well as basic object tracking.

One or more stereo cameras 868 may also be included in a front-facing configuration. The stereo camera(s) 868 may include an integrated control unit comprising a scalable processing unit, which may provide a programmable logic (FPGA) and a multi-core micro-processor with an integrated CAN or Ethernet interface on a single chip. Such a unit may be used to generate a 3-D map of the vehicle's environment, including a distance estimate for all the points in the image. An alternative stereo camera(s) 868 may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s) 868 may be used in addition to, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment to the side of the vehicle 800 (e.g., side-view cameras) may be used for surround view, providing information used to create and update the occupancy grid, as well as to generate side impact collision warnings. For example, surround camera(s) 874 (e.g., four surround cameras 874 as illustrated in FIG. 8B) may be positioned to on the vehicle 800. The surround camera(s) 874 may include wide-view camera(s) 870, fisheye camera(s), 360 degree camera(s), and/or the like. Four example, four fisheye cameras may be positioned on the vehicle's front, rear, and sides. In an alternative arrangement, the vehicle may use three surround camera(s) 874 (e.g., left, right, and rear), and may leverage one or more other camera(s) (e.g., a forward-facing camera) as a fourth surround view camera.

Cameras with a field of view that include portions of the environment to the rear of the vehicle 800 (e.g., rear-view cameras) may be used for park assistance, surround view, rear collision warnings, and creating and updating the occupancy grid. A wide variety of cameras may be used including, but not limited to, cameras that are also suitable as a front-facing camera(s) (e.g., long-range and/or mid-range camera(s) 898, stereo camera(s) 868), infrared camera(s) 872, etc.), as described herein.

FIG. 8C is a block diagram of an example system architecture for the example autonomous vehicle 800 of FIG. 8A, in accordance with some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory.

Each of the components, features, and systems of the vehicle 800 in FIG. 8C are illustrated as being connected via bus 802. The bus 802 may include a Controller Area Network (CAN) data interface (alternatively referred to herein as a “CAN bus”). A CAN may be a network inside the vehicle 800 used to aid in control of various features and functionality of the vehicle 800, such as actuation of brakes, acceleration, braking, steering, windshield wipers, etc. A CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine revolutions per minute (RPMs), button positions, and/or other vehicle status indicators. The CAN bus may be ASIL B compliant.

Although the bus 802 is described herein as being a CAN bus, this is not intended to be limiting. For example, in addition to, or alternatively from, the CAN bus, FlexRay and/or Ethernet may be used. Additionally, although a single line is used to represent the bus 802, this is not intended to be limiting. For example, there may be any number of busses 802, which may include one or more CAN busses, one or more FlexRay busses, one or more Ethernet busses, and/or one or more other types of busses using a different protocol. In some examples, two or more busses 802 may be used to perform different functions, and/or may be used for redundancy. For example, a first bus 802 may be used for collision avoidance functionality and a second bus 802 may be used for actuation control. In any example, each bus 802 may communicate with any of the components of the vehicle 800, and two or more busses 802 may communicate with the same components. In some examples, each SoC 804, each controller 836, and/or each computer within the vehicle may have access to the same input data (e.g., inputs from sensors of the vehicle 800), and may be connected to a common bus, such the CAN bus.

The vehicle 800 may include one or more controller(s) 836, such as those described herein with respect to FIG. 8A. The controller(s) 836 may be used for a variety of functions. The controller(s) 836 may be coupled to any of the various other components and systems of the vehicle 800, and may be used for control of the vehicle 800, artificial intelligence of the vehicle 800, infotainment for the vehicle 800, and/or the like.

The vehicle 800 may include a system(s) on a chip (SoC) 804. The SoC 804 may include CPU(s) 806, GPU(s) 808, processor(s) 810, cache(s) 812, accelerator(s) 814, data store(s) 816, and/or other components and features not illustrated. The SoC(s) 804 may be used to control the vehicle 800 in a variety of platforms and systems. For example, the SoC(s) 804 may be combined in a system (e.g., the system of the vehicle 800) with an HD map 822 which may obtain map refreshes and/or updates via a network interface 824 from one or more servers (e.g., server(s) 878 of FIG. 8D).

The CPU(s) 806 may include a CPU cluster or CPU complex (alternatively referred to herein as a “CCPLEX”). The CPU(s) 806 may include multiple cores and/or L2 caches. For example, in some embodiments, the CPU(s) 806 may include eight cores in a coherent multi-processor configuration. In some embodiments, the CPU(s) 806 may include four dual-core clusters where each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s) 806 (e.g., the CCPLEX) may be configured to support simultaneous cluster operation enabling any combination of the clusters of the CPU(s) 806 to be active at any given time.

The CPU(s) 806 may implement power management capabilities that include one or more of the following features: individual hardware blocks may be clock-gated automatically when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to execution of WFI/WFE instructions; each core may be independently power-gated; each core cluster may be independently clock-gated when all cores are clock-gated or power-gated; and/or each core cluster may be independently power-gated when all cores are power-gated. The CPU(s) 806 may further implement an enhanced algorithm for managing power states, where allowed power states and expected wakeup times are specified, and the hardware/microcode determines the best power state to enter for the core, cluster, and CCPLEX. The processing cores may support simplified power state entry sequences in software with the work offloaded to microcode.

The GPU(s) 808 may include an integrated GPU (alternatively referred to herein as an “iGPU”). The GPU(s) 808 may be programmable and may be efficient for parallel workloads. The GPU(s) 808, in some examples, may use an enhanced tensor instruction set. The GPU(s) 808 may include one or more streaming microprocessors, where each streaming microprocessor may include an L1 cache (e.g., an L1 cache with at least 96 KB storage capacity), and two or more of the streaming microprocessors may share an L2 cache (e.g., an L2 cache with a 512 KB storage capacity). In some embodiments, the GPU(s) 808 may include at least eight streaming microprocessors. The GPU(s) 808 may use compute application programming interface(s) (API(s)). In addition, the GPU(s) 808 may use one or more parallel computing platforms and/or programming models (e.g., NVIDIA's CUDA).

The GPU(s) 808 may be power-optimized for best performance in automotive and embedded use cases. For example, the GPU(s) 808 may be fabricated on a Fin field-effect transistor (FinFET). However, this is not intended to be limiting and the GPU(s) 808 may be fabricated using other semiconductor manufacturing processes. Each streaming microprocessor may incorporate a number of mixed-precision processing cores partitioned into multiple blocks. For example, and without limitation, 64 PF32 cores and 32 PF64 cores may be partitioned into four processing blocks. In such an example, each processing block may be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs for deep learning matrix arithmetic, an L0 instruction cache, a warp scheduler, a dispatch unit, and/or a 64 KB register file. In addition, the streaming microprocessors may include independent parallel integer and floating-point data paths to provide for efficient execution of workloads with a mix of computation and addressing calculations. The streaming microprocessors may include independent thread scheduling capability to enable finer-grain synchronization and cooperation between parallel threads. The streaming microprocessors may include a combined L1 data cache and shared memory unit in order to improve performance while simplifying programming.

The GPU(s) 808 may include a high bandwidth memory (HBM) and/or a 16 GB HBM2 memory subsystem to provide, in some examples, about 900 GB/second peak memory bandwidth. In some examples, in addition to, or alternatively from, the HBM memory, a synchronous graphics random-access memory (SGRAM) may be used, such as a graphics double data rate type five synchronous random-access memory (GDDR5).

The GPU(s) 808 may include unified memory technology including access counters to allow for more accurate migration of memory pages to the processor that accesses them most frequently, thereby improving efficiency for memory ranges shared between processors. In some examples, address translation services (ATS) support may be used to allow the GPU(s) 808 to access the CPU(s) 806 page tables directly. In such examples, when the GPU(s) 808 memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU(s) 806. In response, the CPU(s) 806 may look in its page tables for the virtual-to-physical mapping for the address and transmits the translation back to the GPU(s) 808. As such, unified memory technology may allow a single unified virtual address space for memory of both the CPU(s) 806 and the GPU(s) 808, thereby simplifying the GPU(s) 808 programming and porting of applications to the GPU(s) 808.

In addition, the GPU(s) 808 may include an access counter that may keep track of the frequency of access of the GPU(s) 808 to memory of other processors. The access counter may help ensure that memory pages are moved to the physical memory of the processor that is accessing the pages most frequently.

The SoC(s) 804 may include any number of cache(s) 812, including those described herein. For example, the cache(s) 812 may include an L3 cache that is available to both the CPU(s) 806 and the GPU(s) 808 (e.g., that is connected both the CPU(s) 806 and the GPU(s) 808). The cache(s) 812 may include a write-back cache that may keep track of states of lines, such as by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may include 4 MB or more, depending on the embodiment, although smaller cache sizes may be used.

The SoC(s) 804 may include an arithmetic logic unit(s) (ALU(s)) which may be leveraged in performing processing with respect to any of the variety of tasks or operations of the vehicle 800—such as processing DNNs. In addition, the SoC(s) 804 may include a floating point unit(s) (FPU(s))—or other math coprocessor or numeric coprocessor types—for performing mathematical operations within the system. For example, the SoC(s) 104 may include one or more FPUs integrated as execution units within a CPU(s) 806 and/or GPU(s) 808.

The SoC(s) 804 may include one or more accelerators 814 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, the SoC(s) 804 may include a hardware acceleration cluster that may include optimized hardware accelerators and/or large on-chip memory. The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to complement the GPU(s) 808 and to off-load some of the tasks of the GPU(s) 808 (e.g., to free up more cycles of the GPU(s) 808 for performing other tasks). As an example, the accelerator(s) 814 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are stable enough to be amenable to acceleration. The term “CNN,” as used herein, may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 814 (e.g., the hardware acceleration cluster) may include a deep learning accelerator(s) (DLA). The DLA(s) may include one or more Tensor processing units (TPUs) that may be configured to provide an additional ten trillion operations per second for deep learning applications and inferencing. The TPUs may be accelerators configured to, and optimized for, performing image processing functions (e.g., for CNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specific set of neural network types and floating point operations, as well as inferencing. The design of the DLA(s) may provide more performance per millimeter than a general-purpose GPU, and vastly exceeds the performance of a CPU. The TPU(s) may perform several functions, including a single-instance convolution function, supporting, for example, INT8, INT16, and FP16 data types for both features and weights, as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks, especially CNNs, on processed or unprocessed data for any of a variety of functions, including, for example and without limitation: a CNN for object identification and detection using data from camera sensors; a CNN for distance estimation using data from camera sensors; a CNN for emergency vehicle detection and identification and detection using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and/or a CNN for security and/or safety related events.

The DLA(s) may perform any function of the GPU(s) 808, and by using an inference accelerator, for example, a designer may target either the DLA(s) or the GPU(s) 808 for any function. For example, the designer may focus processing of CNNs and floating point operations on the DLA(s) and leave other functions to the GPU(s) 808 and/or other accelerator(s) 814.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) may include a programmable vision accelerator(s) (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA(s) may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and/or any number of vector processors.

The RISC cores may interact with image sensors (e.g., the image sensors of any of the cameras described herein), image signal processor(s), and/or the like. Each of the RISC cores may include any amount of memory. The RISC cores may use any of a number of protocols, depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and/or memory devices. For example, the RISC cores may include an instruction cache and/or a tightly coupled RAM.

The DMA may enable components of the PVA(s) to access the system memory independently of the CPU(s) 806. The DMA may support any number of features used to provide optimization to the PVA including, but not limited to, supporting multi-dimensional addressing and/or circular addressing. In some examples, the DMA may support up to six or more dimensions of addressing, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and/or depth stepping.

The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMA engines), and/or other peripherals. The vector processing subsystem may operate as the primary processing engine of the PVA, and may include a vector processing unit (VPU), an instruction cache, and/or vector memory (e.g., VMEM). A VPU core may include a digital signal processor such as, for example, a single instruction, multiple data (SIMM), very long instruction word (VLIW) digital signal processor. The combination of the SIMD and VLIW may enhance throughput and speed.

Each of the vector processors may include an instruction cache and may be coupled to dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors that are included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, the plurality of vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may simultaneously execute different computer vision algorithms, on the same image, or even execute different algorithms on sequential images or portions of an image. Among other things, any number of PVAs may be included in the hardware acceleration cluster and any number of vector processors may be included in each of the PVAs. In addition, the PVA(s) may include additional error correcting code (ECC) memory, to enhance overall system safety.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) may include a computer vision network on-chip and SRAM, for providing a high-bandwidth, low latency SRAM for the accelerator(s) 814. In some examples, the on-chip memory may include at least 4 MB SRAM, consisting of, for example and without limitation, eight field-configurable memory blocks, that may be accessible by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides the PVA and DLA with high-speed access to memory. The backbone may include a computer vision network on-chip that interconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface that determines, before transmission of any control signal/address/data, that both the PVA and the DLA provide ready and valid signals. Such an interface may provide for separate phases and separate channels for transmitting control signals/addresses/data, as well as burst-type communications for continuous data transfer. This type of interface may comply with ISO 26262 or IEC 61508 standards, although other standards and protocols may be used.

In some examples, the SoC(s) 804 may include a real-time ray-tracing hardware accelerator, such as described in U.S. patent application Ser. No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), to generate real-time visualization simulations, for RADAR signal interpretation, for sound propagation synthesis and/or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison to LIDAR data for purposes of localization and/or other functions, and/or for other uses. In some embodiments, one or more tree traversal units (TTUs) may be used for executing one or more ray-tracing related operations.

The accelerator(s) 814 (e.g., the hardware accelerator cluster) have a wide array of uses for autonomous driving. The PVA may be a programmable vision accelerator that may be used for key processing stages in ADAS and autonomous vehicles. The PVA's capabilities are a good match for algorithmic domains needing predictable processing, at low power and low latency. In other words, the PVA performs well on semi-dense or dense regular computation, even on small data sets, which need predictable run-times with low latency and low power. Thus, in the context of platforms for autonomous vehicles, the PVAs are designed to run classic computer vision algorithms, as they are efficient at object detection and operating on integer math.

For example, according to one embodiment of the technology, the PVA is used to perform computer stereo vision. A semi-global matching-based algorithm may be used in some examples, although this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require motion estimation/stereo matching on-the-fly (e.g., structure from motion, pedestrian recognition, lane detection, etc.). The PVA may perform computer stereo vision function on inputs from two monocular cameras.

In some examples, the PVA may be used to perform dense optical flow. According to process raw RADAR data (e.g., using a 4D Fast Fourier Transform) to provide Processed RADAR. In other examples, the PVA is used for time of flight depth processing, by processing raw time of flight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control and driving safety, including for example, a neural network that outputs a measure of confidence for each object detection. Such a confidence value may be interpreted as a probability, or as providing a relative “weight” of each detection compared to other detections. This confidence value enables the system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections. For example, the system may set a threshold value for the confidence and consider only the detections exceeding the threshold value as true positive detections. In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may run a neural network for regressing the confidence value. The neural network may take as its input at least some subset of parameters, such as bounding box dimensions, ground plane estimate obtained (e.g. from another subsystem), inertial measurement unit (IMU) sensor 866 output that correlates with the vehicle 800 orientation, distance, 3D location estimates of the object obtained from the neural network and/or other sensors (e.g., LIDAR sensor(s) 864 or RADAR sensor(s) 860), among others.

The SoC(s) 804 may include data store(s) 816 (e.g., memory). The data store(s) 816 may be on-chip memory of the SoC(s) 804, which may store neural networks to be executed on the GPU and/or the DLA. In some examples, the data store(s) 816 may be large enough in capacity to store multiple instances of neural networks for redundancy and safety. The data store(s) 812 may comprise L2 or L3 cache(s) 812. Reference to the data store(s) 816 may include reference to the memory associated with the PVA, DLA, and/or other accelerator(s) 814, as described herein.

The SoC(s) 804 may include one or more processor(s) 810 (e.g., embedded processors). The processor(s) 810 may include a boot and power management processor that may be a dedicated processor and subsystem to handle boot power and management functions and related security enforcement. The boot and power management processor may be a part of the SoC(s) 804 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, assistance in system low power state transitions, management of SoC(s) 804 thermals and temperature sensors, and/or management of the SoC(s) 804 power states. Each temperature sensor may be implemented as a ring-oscillator whose output frequency is proportional to temperature, and the SoC(s) 804 may use the ring-oscillators to detect temperatures of the CPU(s) 806, GPU(s) 808, and/or accelerator(s) 814. If temperatures are determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and put the SoC(s) 804 into a lower power state and/or put the vehicle 800 into a chauffeur to safe stop mode (e.g., bring the vehicle 800 to a safe stop).

The processor(s) 810 may further include a set of embedded processors that may serve as an audio processing engine. The audio processing engine may be an audio subsystem that enables full hardware support for multi-channel audio over multiple interfaces, and a broad and flexible range of audio I/O interfaces. In some examples, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM.

The processor(s) 810 may further include an always on processor engine that may provide necessary hardware features to support low power sensor management and wake use cases. The always on processor engine may include a processor core, a tightly coupled RAM, supporting peripherals (e.g., timers and interrupt controllers), various I/O controller peripherals, and routing logic.

The processor(s) 810 may further include a safety cluster engine that includes a dedicated processor subsystem to handle safety management for automotive applications. The safety cluster engine may include two or more processor cores, a tightly coupled RAM, support peripherals (e.g., timers, an interrupt controller, etc.), and/or routing logic. In a safety mode, the two or more cores may operate in a lockstep mode and function as a single core with comparison logic to detect any differences between their operations.

The processor(s) 810 may further include a real-time camera engine that may include a dedicated processor subsystem for handling real-time camera management.

The processor(s) 810 may further include a high-dynamic range signal processor that may include an image signal processor that is a hardware engine that is part of the camera processing pipeline.

The processor(s) 810 may include a video image compositor that may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions needed by a video playback application to produce the final image for the player window. The video image compositor may perform lens distortion correction on wide-view camera(s) 870, surround camera(s) 874, and/or on in-cabin monitoring camera sensors. In-cabin monitoring camera sensor is preferably monitored by a neural network running on another instance of the Advanced SoC, configured to identify in cabin events and respond accordingly. An in-cabin system may perform lip reading to activate cellular service and place a phone call, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are available to the driver only when the vehicle is operating in an autonomous mode, and are disabled otherwise.

The video image compositor may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, where motion occurs in a video, the noise reduction weights spatial information appropriately, decreasing the weight of information provided by adjacent frames. Where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositor may use information from the previous image to reduce noise in the current image.

The video image compositor may also be configured to perform stereo rectification on input stereo lens frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use, and the GPU(s) 808 is not required to continuously render new surfaces. Even when the GPU(s) 808 is powered on and active doing 3D rendering, the video image compositor may be used to offload the GPU(s) 808 to improve performance and responsiveness.

The SoC(s) 804 may further include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from cameras, a high-speed interface, and/or a video input block that may be used for camera and related pixel input functions. The SoC(s) 804 may further include an input/output controller(s) that may be controlled by software and may be used for receiving I/O signals that are uncommitted to a specific role.

The SoC(s) 804 may further include a broad range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and/or other devices. The SoC(s) 804 may be used to process data from cameras (e.g., connected over Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR sensor(s) 864, RADAR sensor(s) 860, etc. that may be connected over Ethernet), data from bus 802 (e.g., speed of vehicle 800, steering wheel position, etc.), data from GNSS sensor(s) 858 (e.g., connected over Ethernet or CAN bus). The SoC(s) 804 may further include dedicated high-performance mass storage controllers that may include their own DMA engines, and that may be used to free the CPU(s) 806 from routine data management tasks.

The SoC(s) 804 may be an end-to-end platform with a flexible architecture that spans automation levels 3-5, thereby providing a comprehensive functional safety architecture that leverages and makes efficient use of computer vision and ADAS techniques for diversity and redundancy, provides a platform for a flexible, reliable driving software stack, along with deep learning tools. The SoC(s) 804 may be faster, more reliable, and even more energy-efficient and space-efficient than conventional systems. For example, the accelerator(s) 814, when combined with the CPU(s) 806, the GPU(s) 808, and the data store(s) 816, may provide for a fast, efficient platform for level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms may be executed on CPUs, which may be configured using high-level programming language, such as the C programming language, to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs are oftentimes unable to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption, for example. In particular, many CPUs are unable to execute complex object detection algorithms in real-time, which is a requirement of in-vehicle ADAS applications, and a requirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPU complex, and a hardware acceleration cluster, the technology described herein allows for multiple neural networks to be performed simultaneously and/or sequentially, and for the results to be combined together to enable Level 3-5 autonomous driving functionality. For example, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 820) may include a text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which the neural network has not been specifically trained. The DLA may further include a neural network that is able to identify, interpret, and provides semantic understanding of the sign, and to pass that semantic understanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously, as is required for Level 3, 4, or 5 driving. For example, a warning sign consisting of “Caution: flashing lights indicate icy conditions,” along with an electric light, may be independently or collectively interpreted by several neural networks. The sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a neural network that has been trained), the text “Flashing lights indicate icy conditions” may be interpreted by a second deployed neural network, which informs the vehicle's path planning software (preferably executing on the CPU Complex) that when flashing lights are detected, icy conditions exist. The flashing light may be identified by operating a third deployed neural network over multiple frames, informing the vehicle's path-planning software of the presence (or absence) of flashing lights. All three neural networks may run simultaneously, such as within the DLA and/or on the GPU(s) 808.

In some examples, a CNN for facial recognition and vehicle owner identification may use data from camera sensors to identify the presence of an authorized driver and/or owner of the vehicle 800. The always on sensor processing engine may be used to unlock the vehicle when the owner approaches the driver door and turn on the lights, and, in security mode, to disable the vehicle when the owner leaves the vehicle. In this way, the SoC(s) 804 provide for security against theft and/or carjacking.

In another example, a CNN for emergency vehicle detection and identification may use data from microphones 896 to detect and identify emergency vehicle sirens. In contrast to conventional systems, that use general classifiers to detect sirens and manually extract features, the SoC(s) 804 use the CNN for classifying environmental and urban sounds, as well as classifying visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing speed of the emergency vehicle (e.g., by using the Doppler Effect). The CNN may also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by GNSS sensor(s) 858. Thus, for example, when operating in Europe the CNN will seek to detect European sirens, and when in the United States the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, a control program may be used to execute an emergency vehicle safety routine, slowing the vehicle, pulling over to the side of the road, parking the vehicle, and/or idling the vehicle, with the assistance of ultrasonic sensors 862, until the emergency vehicle(s) passes.

The vehicle may include a CPU(s) 818 (e.g., discrete CPU(s), or dCPU(s)), that may be coupled to the SoC(s) 804 via a high-speed interconnect (e.g., PCIe). The CPU(s) 818 may include an X86 processor, for example. The CPU(s) 818 may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC(s) 804, and/or monitoring the status and health of the controller(s) 836 and/or infotainment SoC 830, for example.

The vehicle 800 may include a GPU(s) 820 (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s) 804 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 820 may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based on input (e.g., sensor data) from sensors of the vehicle 800.

The vehicle 800 may further include the network interface 824 which may include one or more wireless antennas 826 (e.g., one or more wireless antennas for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). The network interface 824 may be used to enable wireless connectivity over the Internet with the cloud (e.g., with the server(s) 878 and/or other network devices), with other vehicles, and/or with computing devices (e.g., client devices of passengers). To communicate with other vehicles, a direct link may be established between the two vehicles and/or an indirect link may be established (e.g., across networks and over the Internet). Direct links may be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link may provide the vehicle 800 information about vehicles in proximity to the vehicle 800 (e.g., vehicles in front of, on the side of, and/or behind the vehicle 800). This functionality may be part of a cooperative adaptive cruise control functionality of the vehicle 800.

The network interface 824 may include a SoC that provides modulation and demodulation functionality and enables the controller(s) 836 to communicate over wireless networks. The network interface 824 may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband. The frequency conversions may be performed through well-known processes, and/or may be performed using super-heterodyne processes. In some examples, the radio frequency front end functionality may be provided by a separate chip. The network interface may include wireless functionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or other wireless protocols.

The vehicle 800 may further include data store(s) 828 which may include off-chip (e.g., off the SoC(s) 804) storage. The data store(s) 828 may include one or more storage elements including RAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other components and/or devices that may store at least one bit of data.

The vehicle 800 may further include GNSS sensor(s) 858. The GNSS sensor(s) 858 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions. Any number of GNSS sensor(s) 858 may be used, including, for example and without limitation, a GPS using a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 800 may further include RADAR sensor(s) 860. The RADAR sensor(s) 860 may be used by the vehicle 800 for long-range vehicle detection, even in darkness and/or severe weather conditions. RADAR functional safety levels may be ASIL B. The RADAR sensor(s) 860 may use the CAN and/or the bus 802 (e.g., to transmit data generated by the RADAR sensor(s) 860) for control and to access object tracking data, with access to Ethernet to access raw data in some examples. A wide variety of RADAR sensor types may be used. For example, and without limitation, the RADAR sensor(s) 860 may be suitable for front, rear, and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 860 may include different configurations, such as long range with narrow field of view, short range with wide field of view, short range side coverage, etc. In some examples, long-range RADAR may be used for adaptive cruise control functionality. The long-range RADAR systems may provide a broad field of view realized by two or more independent scans, such as within a 250 m range. The RADAR sensor(s) 860 may help in distinguishing between static and moving objects, and may be used by ADAS systems for emergency brake assist and forward collision warning. Long-range RADAR sensors may include monostatic multimodal RADAR with multiple (e.g., six or more) fixed RADAR antennae and a high-speed CAN and FlexRay interface. In an example with six antennae, the central four antennae may create a focused beam pattern, designed to record the vehicle's 800 surroundings at higher speeds with minimal interference from traffic in adjacent lanes. The other two antennae may expand the field of view, making it possible to quickly detect vehicles entering or leaving the vehicle's 800 lane.

Mid-range RADAR systems may include, as an example, a range of up to 860 m (front) or 80 m (rear), and a field of view of up to 42 degrees (front) or 850 degrees (rear). Short-range RADAR systems may include, without limitation, RADAR sensors designed to be installed at both ends of the rear bumper. When installed at both ends of the rear bumper, such a RADAR sensor systems may create two beams that constantly monitor the blind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spot detection and/or lane change assist.

The vehicle 800 may further include ultrasonic sensor(s) 862. The ultrasonic sensor(s) 862, which may be positioned at the front, back, and/or the sides of the vehicle 800, may be used for park assist and/or to create and update an occupancy grid. A wide variety of ultrasonic sensor(s) 862 may be used, and different ultrasonic sensor(s) 862 may be used for different ranges of detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s) 862 may operate at functional safety levels of ASIL B.

The vehicle 800 may include LIDAR sensor(s) 864. The LIDAR sensor(s) 864 may be used for object and pedestrian detection, emergency braking, collision avoidance, and/or other functions. The LIDAR sensor(s) 864 may be functional safety level ASIL B. In some examples, the vehicle 800 may include multiple LIDAR sensors 864 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 864 may be capable of providing a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensor(s) 864 may have an advertised range of approximately 800 m, with an accuracy of 2 cm-3 cm, and with support for a 800 Mbps Ethernet connection, for example. In some examples, one or more non-protruding LIDAR sensors 864 may be used. In such examples, the LIDAR sensor(s) 864 may be implemented as a small device that may be embedded into the front, rear, sides, and/or corners of the vehicle 800. The LIDAR sensor(s) 864, in such examples, may provide up to a 120-degree horizontal and 35-degree vertical field-of-view, with a 200 m range even for low-reflectivity objects. Front-mounted LIDAR sensor(s) 864 may be configured for a horizontal field of view between 45 degrees and 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may also be used. 3D Flash LIDAR uses a flash of a laser as a transmission source, to illuminate vehicle surroundings up to approximately 200m. A flash LIDAR unit includes a receptor, which records the laser pulse transit time and the reflected light on each pixel, which in turn corresponds to the range from the vehicle to the objects. Flash LIDAR may allow for highly accurate and distortion-free images of the surroundings to be generated with every laser flash. In some examples, four flash LIDAR sensors may be deployed, one at each side of the vehicle 800. Available 3D flash LIDAR systems include a solid-state 3D staring array LIDAR camera with no moving parts other than a fan (e.g., a non-scanning LIDAR device). The flash LIDAR device may use a 5 nanosecond class I (eye-safe) laser pulse per frame and may capture the reflected laser light in the form of 3D range point clouds and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor(s) 864 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 866. The IMU sensor(s) 866 may be located at a center of the rear axle of the vehicle 800, in some examples. The IMU sensor(s) 866 may include, for example and without limitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), a magnetic compass(es), and/or other sensor types. In some examples, such as in six-axis applications, the IMU sensor(s) 866 may include accelerometers and gyroscopes, while in nine-axis applications, the IMU sensor(s) 866 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 866 may be implemented as a miniature, high performance GPS-Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude. As such, in some examples, the IMU sensor(s) 866 may enable the vehicle 800 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s) 866. In some examples, the IMU sensor(s) 866 and the GNSS sensor(s) 858 may be combined in a single integrated unit.

The vehicle may include microphone(s) 896 placed in and/or around the vehicle 800. The microphone(s) 896 may be used for emergency vehicle detection and identification, among other things.

The vehicle may further include any number of camera types, including stereo camera(s) 868, wide-view camera(s) 870, infrared camera(s) 872, surround camera(s) 874, long-range and/or mid-range camera(s) 898, and/or other camera types. The cameras may be used to capture image data around an entire periphery of the vehicle 800. The types of cameras used depends on the embodiments and requirements for the vehicle 800, and any combination of camera types may be used to provide the necessary coverage around the vehicle 800. In addition, the number of cameras may differ depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and/or another number of cameras. The cameras may support, as an example and without limitation, Gigabit Multimedia Serial Link (GMSL) and/or Gigabit Ethernet. Each of the camera(s) is described with more detail herein with respect to FIG. 8A and FIG. 8B.

The vehicle 800 may further include vibration sensor(s) 842. The vibration sensor(s) 842 may measure vibrations of components of the vehicle, such as the axle(s). For example, changes in vibrations may indicate a change in road surfaces. In another example, when two or more vibration sensors 842 are used, the differences between the vibrations may be used to determine friction or slippage of the road surface (e.g., when the difference in vibration is between a power-driven axle and a freely rotating axle).

The vehicle 800 may include an ADAS system 838. The ADAS system 838 may include a SoC, in some examples. The ADAS system 838 may include autonomous/adaptive/automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward crash warning (FCW), automatic emergency braking (AEB), lane departure warnings (LDW), lane keep assist (LKA), blind spot warning (BSW), rear cross-traffic warning (RCTW), collision warning systems (CWS), lane centering (LC), and/or other features and functionality.

The ACC systems may use RADAR sensor(s) 860, LIDAR sensor(s) 864, and/or a camera(s). The ACC systems may include longitudinal ACC and/or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately ahead of the vehicle 800 and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead. Lateral ACC performs distance keeping, and advises the vehicle 800 to change lanes when necessary. Lateral ACC is related to other ADAS applications such as LCA and CWS.

CACC uses information from other vehicles that may be received via the network interface 824 and/or the wireless antenna(s) 826 from other vehicles via a wireless link, or indirectly, over a network connection (e.g., over the Internet). Direct links may be provided by a vehicle-to-vehicle (V2V) communication link, while indirect links may be infrastructure-to-vehicle (I2V) communication link. In general, the V2V communication concept provides information about the immediately preceding vehicles (e.g., vehicles immediately ahead of and in the same lane as the vehicle 800), while the I2V communication concept provides information about traffic further ahead. CACC systems may include either or both I2V and V2V information sources. Given the information of the vehicles ahead of the vehicle 800, CACC may be more reliable and it has potential to improve traffic flow smoothness and reduce congestion on the road.

FCW systems are designed to alert the driver to a hazard, so that the driver may take corrective action. FCW systems use a front-facing camera and/or RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component. FCW systems may provide a warning, such as in the form of a sound, visual warning, vibration and/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision and, if the driver does not take corrective action, the AEB system may automatically apply the brakes in an effort to prevent, or at least mitigate, the impact of the predicted collision. AEB systems, may include techniques such as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 800 crosses lane markings. A LDW system does not activate when the driver indicates an intentional lane departure, by activating a turn signal. LDW systems may use front-side facing cameras, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component.

LKA systems are a variation of LDW systems. LKA systems provide steering input or braking to correct the vehicle 800 if the vehicle 800 starts to exit the lane.

BSW systems detects and warn the driver of vehicles in an automobile's blind spot. BSW systems may provide a visual, audible, and/or tactile alert to indicate that merging or changing lanes is unsafe. The system may provide an additional warning when the driver uses a turn signal. BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notification when an object is detected outside the rear-camera range when the vehicle 800 is backing up. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. RCTW systems may use one or more rear-facing RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results which may be annoying and distracting to a driver, but typically are not catastrophic, because the ADAS systems alert the driver and allow the driver to decide whether a safety condition truly exists and act accordingly. However, in an autonomous vehicle 800, the vehicle 800 itself must, in the case of conflicting results, decide whether to heed the result from a primary computer or a secondary computer (e.g., a first controller 836 or a second controller 836). For example, in some embodiments, the ADAS system 838 may be a backup and/or secondary computer for providing perception information to a backup computer rationality module. The backup computer rationality monitor may run a redundant diverse software on hardware components to detect faults in perception and dynamic driving tasks. Outputs from the ADAS system 838 may be provided to a supervisory MCU. If outputs from the primary computer and the secondary computer conflict, the supervisory MCU must determine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide the supervisory MCU with a confidence score, indicating the primary computer's confidence in the chosen result. If the confidence score exceeds a threshold, the supervisory MCU may follow the primary computer's direction, regardless of whether the secondary computer provides a conflicting or inconsistent result. Where the confidence score does not meet the threshold, and where the primary and secondary computer indicate different results (e.g., the conflict), the supervisory MCU may arbitrate between the computers to determine the appropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that is trained and configured to determine, based on outputs from the primary computer and the secondary computer, conditions under which the secondary computer provides false alarms. Thus, the neural network(s) in the supervisory MCU may learn when the secondary computer's output may be trusted, and when it cannot. For example, when the secondary computer is a RADAR-based FCW system, a neural network(s) in the supervisory MCU may learn when the FCW system is identifying metallic objects that are not, in fact, hazards, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the secondary computer is a camera-based LDW system, a neural network in the supervisory MCU may learn to override the LDW when bicyclists or pedestrians are present and a lane departure is, in fact, the safest maneuver. In embodiments that include a neural network(s) running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or GPU suitable for running the neural network(s) with associated memory. In preferred embodiments, the supervisory MCU may comprise and/or be included as a component of the SoC(s) 804.

In other examples, ADAS system 838 may include a secondary computer that performs ADAS functionality using traditional rules of computer vision. As such, the secondary computer may use classic computer vision rules (if-then), and the presence of a neural network(s) in the supervisory MCU may improve reliability, safety and performance. For example, the diverse implementation and intentional non-identity makes the overall system more fault-tolerant, especially to faults caused by software (or software-hardware interface) functionality. For example, if there is a software bug or error in the software running on the primary computer, and the non-identical software code running on the secondary computer provides the same overall result, the supervisory MCU may have greater confidence that the overall result is correct, and the bug in software or hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 838 may be fed into the primary computer's perception block and/or the primary computer's dynamic driving task block. For example, if the ADAS system 838 indicates a forward crash warning due to an object immediately ahead, the perception block may use this information when identifying objects. In other examples, the secondary computer may have its own neural network which is trained and thus reduces the risk of false positives, as described herein.

The vehicle 800 may further include the infotainment SoC 830 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as a SoC, the infotainment system may not be a SoC, and may include two or more discrete components. The infotainment SoC 830 may include a combination of hardware and software that may be used to provide audio (e.g., music, a personal digital assistant, navigational instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and/or information services (e.g., navigation systems, rear-parking assistance, a radio data system, vehicle related information such as fuel level, total distance covered, brake fuel level, oil level, door open/close, air filter information, etc.) to the vehicle 800. For example, the infotainment SoC 830 may radios, disk players, navigation systems, video players, USB and Bluetooth connectivity, carputers, in-car entertainment, Wi-Fi, steering wheel audio controls, hands free voice control, a heads-up display (HUD), an HMI display 834, a telematics device, a control panel (e.g., for controlling and/or interacting with various components, features, and/or systems), and/or other components. The infotainment SoC 830 may further be used to provide information (e.g., visual and/or audible) to a user(s) of the vehicle, such as information from the ADAS system 838, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and/or other information.

The infotainment SoC 830 may include GPU functionality. The infotainment SoC 830 may communicate over the bus 802 (e.g., CAN bus, Ethernet, etc.) with other devices, systems, and/or components of the vehicle 800. In some examples, the infotainment SoC 830 may be coupled to a supervisory MCU such that the GPU of the infotainment system may perform some self-driving functions in the event that the primary controller(s) 836 (e.g., the primary and/or backup computers of the vehicle 800) fail. In such an example, the infotainment SoC 830 may put the vehicle 800 into a chauffeur to safe stop mode, as described herein.

The vehicle 800 may further include an instrument cluster 832 (e.g., a digital dash, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 832 may include a controller and/or supercomputer (e.g., a discrete controller or supercomputer). The instrument cluster 832 may include a set of instrumentation such as a speedometer, fuel level, oil pressure, tachometer, odometer, turn indicators, gearshift position indicator, seat belt warning light(s), parking-brake warning light(s), engine-malfunction light(s), airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and/or shared among the infotainment SoC 830 and the instrument cluster 832. In other words, the instrument cluster 832 may be included as part of the infotainment SoC 830, or vice versa.

FIG. 8D is a system diagram for communication between cloud-based server(s) and the example autonomous vehicle 800 of FIG. 8A, in accordance with some embodiments of the present disclosure. The system 876 may include server(s) 878, network(s) 890, and vehicles, including the vehicle 800. The server(s) 878 may include a plurality of GPUs 884(A)-884(H) (collectively referred to herein as GPUs 884), PCIe switches 882(A)-882(H) (collectively referred to herein as PCIe switches 882), and/or CPUs 880(A)-880(B) (collectively referred to herein as CPUs 880). The GPUs 884, the CPUs 880, and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces 888 developed by NVIDIA and/or PCIe connections 886. In some examples, the GPUs 884 are connected via NVLink and/or NVSwitch SoC and the GPUs 884 and the PCIe switches 882 are connected via PCIe interconnects. Although eight GPUs 884, two CPUs 880, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s) 878 may include any number of GPUs 884, CPUs 880, and/or PCIe switches. For example, the server(s) 878 may each include eight, sixteen, thirty-two, and/or more GPUs 884.

The server(s) 878 may receive, over the network(s) 890 and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work. The server(s) 878 may transmit, over the network(s) 890 and to the vehicles, neural networks 892, updated neural networks 892, and/or map information 894, including information regarding traffic and road conditions. The updates to the map information 894 may include updates for the HD map 822, such as information regarding construction sites, potholes, detours, flooding, and/or other obstructions. In some examples, the neural networks 892, the updated neural networks 892, and/or the map information 894 may have resulted from new training and/or experiences represented in data received from any number of vehicles in the environment, and/or based on training performed at a datacenter (e.g., using the server(s) 878 and/or other servers).

The server(s) 878 may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., where the neural network benefits from supervised learning) and/or undergoes other pre-processing, while in other examples the training data is not tagged and/or pre-processed (e.g., where the neural network does not require supervised learning). Training may be executed according to any one or more classes of machine learning techniques, including, without limitation, classes such as: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analyses), multi-linear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations therefor. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s) 890, and/or the machine learning models may be used by the server(s) 878 to remotely monitor the vehicles.

In some examples, the server(s) 878 may receive data from the vehicles and apply the data to up-to-date real-time neural networks for real-time intelligent inferencing. The server(s) 878 may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s) 884, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s) 878 may include deep learning infrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 878 may be capable of fast, real-time inferencing, and may use that capability to evaluate and verify the health of the processors, software, and/or associated hardware in the vehicle 800. For example, the deep-learning infrastructure may receive periodic updates from the vehicle 800, such as a sequence of images and/or objects that the vehicle 800 has located in that sequence of images (e.g., via computer vision and/or other machine learning object classification techniques). The deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle 800 and, if the results do not match and the infrastructure concludes that the AI in the vehicle 800 is malfunctioning, the server(s) 878 may transmit a signal to the vehicle 800 instructing a fail-safe computer of the vehicle 800 to assume control, notify the passengers, and complete a safe parking maneuver.

For inferencing, the server(s) 878 may include the GPU(s) 884 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and inference acceleration may make real-time responsiveness possible. In other examples, such as where performance is less critical, servers powered by CPUs, FPGAs, and other processors may be used for inferencing.

Example Computing Device

FIG. 9 is a block diagram of an example computing device(s) 900 suitable for use in implementing some embodiments of the present disclosure. Computing device 900 may include an interconnect system 902 that directly or indirectly couples the following devices: memory 904, one or more central processing units (CPUs) 906, one or more graphics processing units (GPUs) 908, a communication interface 910, input/output (I/O) ports 912, input/output components 914, a power supply 916, one or more presentation components 918 (e.g., display(s)), and one or more logic units 920. In at least one embodiment, the computing device(s) 900 may comprise one or more virtual machines (VMs), and/or any of the components thereof may comprise virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs 908 may comprise one or more vGPUs, one or more of the CPUs 906 may comprise one or more vCPUs, and/or one or more of the logic units 920 may comprise one or more virtual logic units. As such, a computing device(s) 900 may include discrete components (e.g., a full GPU dedicated to the computing device 900), virtual components (e.g., a portion of a GPU dedicated to the computing device 900), or a combination thereof.

Although the various blocks of FIG. 9 are shown as connected via the interconnect system 902 with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component 918, such as a display device, may be considered an I/O component 914 (e.g., if the display is a touch screen). As another example, the CPUs 906 and/or GPUs 908 may include memory (e.g., the memory 904 may be representative of a storage device in addition to the memory of the GPUs 908, the CPUs 906, and/or other components). In other words, the computing device of FIG. 9 is merely illustrative. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “game console,” “electronic control unit (ECU),” “virtual reality system,” and/or other device or system types, as all are contemplated within the scope of the computing device of FIG. 9 .

The interconnect system 902 may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system 902 may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and/or another type of bus or link. In some embodiments, there are direct connections between components. As an example, the CPU 906 may be directly connected to the memory 904. Further, the CPU 906 may be directly connected to the GPU 908. Where there is direct, or point-to-point connection between components, the interconnect system 902 may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device 900.

The memory 904 may include any of a variety of computer-readable media. The computer-readable media may be any available media that may be accessed by the computing device 900. The computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer-storage media and communication media.

The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory 904 may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device 900. As used herein, computer storage media does not comprise signals per se.

The computer storage media may embody computer-readable instructions, data structures, program modules, and/or other data types in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, the computer storage media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

The CPU(s) 906 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and/or processes described herein. The CPU(s) 906 may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) that are capable of handling a multitude of software threads simultaneously. The CPU(s) 906 may include any type of processor, and may include different types of processors depending on the type of computing device 900 implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device 900, the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 900 may include one or more CPUs 906 in addition to one or more microprocessors or supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 906, the GPU(s) 908 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and/or processes described herein. One or more of the GPU(s) 908 may be an integrated GPU (e.g., with one or more of the CPU(s) 906 and/or one or more of the GPU(s) 908 may be a discrete GPU. In embodiments, one or more of the GPU(s) 908 may be a coprocessor of one or more of the CPU(s) 906. The GPU(s) 908 may be used by the computing device 900 to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s) 908 may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 908 may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s) 908 may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s) 906 received via a host interface). The GPU(s) 908 may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory 904. The GPU(s) 908 may include two or more GPUs operating in parallel (e.g., via a link). The link may directly connect the GPUs (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU 908 may generate pixel data or GPGPU data for different portions of an output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 906 and/or the GPU(s) 908, the logic unit(s) 920 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900 to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s) 906, the GPU(s) 908, and/or the logic unit(s) 920 may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units 920 may be part of and/or integrated in one or more of the CPU(s) 906 and/or the GPU(s) 908 and/or one or more of the logic units 920 may be discrete components or otherwise external to the CPU(s) 906 and/or the GPU(s) 908. In embodiments, one or more of the logic units 920 may be a coprocessor of one or more of the CPU(s) 906 and/or one or more of the GPU(s) 908.

Examples of the logic unit(s) 920 include one or more processing cores and/or components thereof, such as Data Processing Units (DPUs), Tensor Cores (TCs), Tensor Processing Units (TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), Artificial Intelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits (ASICs), Floating Point Units (FPUs), input/output (I/O) elements, peripheral component interconnect (PCI) or peripheral component interconnect express (PCIe) elements, and/or the like.

The communication interface 910 may include one or more receivers, transmitters, and/or transceivers that enable the computing device 900 to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface 910 may include components and functionality to enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet. In one or more embodiments, logic unit(s) 920 and/or communication interface 910 may include one or more data processing units (DPUs) to transmit data received over a network and/or through interconnect system 902 directly to (e.g., a memory of) one or more GPU(s) 908.

The I/O ports 912 may enable the computing device 900 to be logically coupled to other devices including the I/O components 914, the presentation component(s) 918, and/or other components, some of which may be built in to (e.g., integrated in) the computing device 900. Illustrative I/O components 914 include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components 914 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with a display of the computing device 900. The computing device 900 may be include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, the computing device 900 may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable detection of motion. In some examples, the output of the accelerometers or gyroscopes may be used by the computing device 900 to render immersive augmented reality or virtual reality.

The power supply 916 may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply 916 may provide power to the computing device 900 to enable the components of the computing device 900 to operate.

The presentation component(s) 918 may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up-display (HUD), other display types, or a combination thereof), speakers, and/or other presentation components. The presentation component(s) 918 may receive data from other components (e.g., the GPU(s) 908, the CPU(s) 906, DPUs, etc.), and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG. 10 illustrates an example data center 1000 that may be used in at least one embodiments of the present disclosure. The data center 1000 may include a data center infrastructure layer 1010, a framework layer 1020, a software layer 1030, and/or an application layer 1040.

As shown in FIG. 10 , the data center infrastructure layer 1010 may include a resource orchestrator 1012, grouped computing resources 1014, and node computing resources (“node C.R.s”) 1016(1)-1016(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s 1016(1)-1016(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (NW I/O) devices, network switches, virtual machines (VMs), power modules, and/or cooling modules, etc. In some embodiments, one or more node C.R.s from among node C.R.s 1016(1)-1016(N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node C.R.s 1016(1)-10161(N) may include one or more virtual components, such as vGPUs, vCPUs, and/or the like, and/or one or more of the node C.R.s 1016(1)-1016(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 1014 may include separate groupings of node C.R.s 1016 housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s 1016 within grouped computing resources 1014 may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s 1016 including CPUs, GPUs, DPUs, and/or other processors may be grouped within one or more racks to provide compute resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and/or network switches, in any combination.

The resource orchestrator 1012 may configure or otherwise control one or more node C.R.s 1016(1)-1016(N) and/or grouped computing resources 1014. In at least one embodiment, resource orchestrator 1012 may include a software design infrastructure (SDI) management entity for the data center 1000. The resource orchestrator 1012 may include hardware, software, or some combination thereof.

In at least one embodiment, as shown in FIG. 10 , framework layer 1020 may include a job scheduler 1032, a configuration manager 1034, a resource manager 1036, and/or a distributed file system 1038. The framework layer 1020 may include a framework to support software 1032 of software layer 1030 and/or one or more application(s) 1042 of application layer 1040. The software 1032 or application(s) 1042 may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. The framework layer 1020 may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system 1038 for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler 1032 may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center 1000. The configuration manager 1034 may be capable of configuring different layers such as software layer 1030 and framework layer 1020 including Spark and distributed file system 1038 for supporting large-scale data processing. The resource manager 1036 may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system 1038 and job scheduler 1032. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource 1014 at data center infrastructure layer 1010. The resource manager 1036 may coordinate with resource orchestrator 1012 to manage these mapped or allocated computing resources.

In at least one embodiment, software 1032 included in software layer 1030 may include software used by at least portions of node C.R.s 1016(1)-1016(N), grouped computing resources 1014, and/or distributed file system 1038 of framework layer 1020. One or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s) 1042 included in application layer 1040 may include one or more types of applications used by at least portions of node C.R.s 1016(1)-1016(N), grouped computing resources 1014, and/or distributed file system 1038 of framework layer 1020. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and/or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager 1034, resource manager 1036, and resource orchestrator 1012 may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. Self-modifying actions may relieve a data center operator of data center 1000 from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

The data center 1000 may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, a machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and/or computing resources described above with respect to the data center 1000. In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to the data center 1000 by using weight parameters calculated through one or more training techniques, such as but not limited to those described herein.

In at least one embodiment, the data center 1000 may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, and/or other hardware (or virtual compute resources corresponding thereto) to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of the disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and/or other device types. The client devices, servers, and/or other device types (e.g., each device) may be implemented on one or more instances of the computing device(s) 900 of FIG. 9 —e.g., each device may include similar components, features, and/or functionality of the computing device(s) 900. In addition, where backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be included as part of a data center 1000, an example of which is described in more detail herein with respect to FIG. 10 .

Components of a network environment may communicate with each other via a network(s), which may be wired, wireless, or both. The network may include multiple networks, or a network of networks. By way of example, the network may include one or more Wide Area Networks (WANs), one or more Local Area Networks (LANs), one or more public networks such as the Internet and/or a public switched telephone network (PSTN), and/or one or more private networks. Where the network includes a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peer network environments—in which case a server may not be included in a network environment—and one or more client-server network environments—in which case one or more servers may be included in a network environment. In peer-to-peer network environments, functionality described herein with respect to a server(s) may be implemented on any number of client devices.

In at least one embodiment, a network environment may include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more of servers, which may include one or more core network servers and/or edge servers. A framework layer may include a framework to support software of a software layer and/or one or more application(s) of an application layer. The software or application(s) may respectively include web-based service software or applications. In embodiments, one or more of the client devices may use the web-based service software or applications (e.g., by accessing the service software and/or applications via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open-source software web application framework such as that may use a distributed file system for large-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/or cloud storage that carries out any combination of computing and/or data storage functions described herein (or one or more portions thereof). Any of these various functions may be distributed over multiple locations from central or core servers (e.g., of one or more data centers that may be distributed across a state, a region, a country, the globe, etc.). If a connection to a user (e.g., a client device) is relatively close to an edge server(s), a core server(s) may designate at least a portion of the functionality to the edge server(s). A cloud-based network environment may be private (e.g., limited to a single organization), may be public (e.g., available to many organizations), and/or a combination thereof (e.g., a hybrid cloud environment).

The client device(s) may include at least some of the components, features, and functionality of the example computing device(s) 900 described herein with respect to FIG. 9 . By way of example and not limitation, a client device may be embodied as a Personal Computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a Personal Digital Assistant (PDA), an MP3 player, a virtual reality headset, a Global Positioning System (GPS) or device, a video player, a video camera, a surveillance device or system, a vehicle, a boat, a flying vessel, a virtual machine, a drone, a robot, a handheld communications device, a hospital device, a gaming device or system, an entertainment system, a vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to code that perform particular tasks or implement particular abstract data types. The disclosure may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network.

As used herein, a recitation of “and/or” with respect to two or more elements should be interpreted to mean only one element, or a combination of elements. For example, “element A, element B, and/or element C” may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, “at least one of element A or element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, “at least one of element A and element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

The subject matter of the present disclosure is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described. 

What is claimed is:
 1. A processor comprising: one or more circuits to: compute, using a neural network and based at least in part on sensor data generated using one or more sensors, one or more output masks, each output mask of the one or more output masks corresponding to an object class and a lane identifier; determine at least one object class label and at least one lane identifier label for one or more points represented by the sensor data based at least in part on the one or more output masks; determine a bounding shape corresponding to at least one object based at least in part on the sensor data; associate the one or more points with the bounding shape; and assign an object class and a lane identifier to the object based at least in part on the at least one object class label and the at least one lane identifier label associated with the one or more points.
 2. The processor of claim 1, wherein the determination of the at least one bounding shape is based at least in part on at least one of: one or more outputs of the neural network representative of the at least one bounding shape; or an output of an object detection algorithm.
 3. The processor of claim 1, wherein the one or more points include one or more pixels.
 4. The processor of claim 1, wherein the determination of the at least one class label and the at least one lane identifier label includes: determining first confidences corresponding to a first combination of object class and lane identifier of the one or more points in a first output mask of the one or more output masks; determining second confidences corresponding to a second combination of object class and lane identifier of the one or more points in a second output mask of the one or more output masks; and determining the at least one class label and the at least one lane identifier label based at least in part on the first confidences being greater than the second confidences.
 5. The processor of claim 1, wherein the one or more outputs masks correspond to one or more segmentation masks.
 6. The processor of claim 1, wherein the sensor data represents a top-down representation of a sensory field of the one or more sensors in three-dimensional (3D) world space, and the one or more output masks are computed in 3D world space.
 7. The processor of claim 1, wherein the sensor data represents an image in two-dimensional (2D) image space, and the one or more output masks are computed in 2D image space.
 8. The processor of claim 1, wherein the association of the one or more points with the bounding shape includes determining that, out of all points within the bounding shape, the one or more points with the at least one object class label and the at least one lane identifier label include more points than any other sets of points with different object class labels and lane identification labels.
 9. The processor of claim 1, wherein the one or more sensors are associated with an autonomous machine, and the one or more circuits are further to perform one or more operations using the autonomous machine based at least in part on the assignment of the object class and the lane identifier to the object.
 10. The processor of claim 1, wherein the at least one lane identifier label includes one of an ego-lane, a lane left of the ego-lane, a lane right of the ego-lane, or another lane.
 11. The processor of claim 1, wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
 12. A system comprising: one or more sensors; one or more processing units; and one or more memory units storing instructions that, when executed by the one or more processing units, cause the one or more processing units to execute operations comprising: computing, using a neural network and based at least in part on image data representative of an image generated using the one or more sensors, one or more output masks, each output mask of the one or more output masks corresponding to a respective combination of object class and lane identifier; determining a first combination of object class and lane identifier corresponding to a first set of pixels of the image and a second combination of object class and lane identifier corresponding to a second set of pixels of the image; determining a bounding shape corresponding to at least one object based at least in part on the sensor data; associating the first set of pixels with the bounding shape based at least in part on the first set of pixels including more pixels than the second set of pixels within the bounding shape; assigning the first combination of object class and lane identifier to the object based at least in part on the association; and performing, using the one or more processing units, one or more operations for an autonomous machine based at least in part on the assignment of the first combination of object class and lane identifier to the object.
 13. The system of claim 12, wherein the image represents a top-down representation of a field of view of the one or more sensors in three-dimensional (3D) world space, and the one or more output masks are computed in 3D world space.
 14. The system of claim 12, wherein the image is in two-dimensional (2D) image space, and the one or more output masks are computed in 2D image space.
 15. The system of claim 12, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
 16. A method comprising: determining one or more bounding shapes corresponding to an image; generating one or more segmentation masks by, for each segmentation mask of the one or more segmentation masks: determining at least one bounding shape of the one or more bounding shapes that corresponds to a respective object class and a lane identifier associated with the segmentation mask; determining at least a subset of pixels of the image corresponding to the at least one bounding shape; and assigning an object class and a lane identifier corresponding to the respective combination of object class and lane identifier to at least the subset of pixels; and training a neural network using the one or more segmentation masks as ground truth data.
 17. The method of claim 16, wherein the determining at least the subset of pixels includes: determining an ellipse within the bounding shape; and determining at least the subset of pixels within the ellipse within the bounding shape.
 18. The method of claim 16, wherein the training the neural network includes computing a loss for each lane identifier type, and computing a sum for each loss of a plurality of lane identifier types.
 19. The method of claim 16, wherein the training the neural network includes computing a loss for each pixel corresponding to each object of one or more objects associated with the one or more bounding shapes.
 20. The method of claim 16, wherein the neural network is trained to compute N×M output channels, where N corresponds to a number of object class labels and M corresponds to a number of lane identifier types. 